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Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data
M. Shafiqur Rahman1 &
Mahbuba Sultana1
When developing risk models for binary data with small or sparse data sets, the standard maximum likelihood estimation (MLE) based logistic regression faces several problems including biased or infinite estimate of the regression coefficient and frequent convergence failure of the likelihood due to separation. The problem of separation occurs commonly even if sample size is large but there is sufficient number of strong predictors. In the presence of separation, even if one develops the model, it produces overfitted model with poor predictive performance. Firth-and logF-type penalized regression methods are popular alternative to MLE, particularly for solving separation-problem. Despite the attractive advantages, their use in risk prediction is very limited. This paper evaluated these methods in risk prediction in comparison with MLE and other commonly used penalized methods such as ridge.
The predictive performance of the methods was evaluated through assessing calibration, discrimination and overall predictive performance using an extensive simulation study. Further an illustration of the methods were provided using a real data example with low prevalence of outcome.
The MLE showed poor performance in risk prediction in small or sparse data sets. All penalized methods offered some improvements in calibration, discrimination and overall predictive performance. Although the Firth-and logF-type methods showed almost equal amount of improvement, Firth-type penalization produces some bias in the average predicted probability, and the amount of bias is even larger than that produced by MLE. Of the logF(1,1) and logF(2,2) penalization, logF(2,2) provides slight bias in the estimate of regression coefficient of binary predictor and logF(1,1) performed better in all aspects. Similarly, ridge performed well in discrimination and overall predictive performance but it often produces underfitted model and has high rate of convergence failure (even the rate is higher than that for MLE), probably due to the separation problem.
The logF-type penalized method, particularly logF(1,1) could be used in practice when developing risk model for small or sparse data sets.
In many areas of clinical research, risk models for binary data are usually developed in the maximum-likelihood (ML) based logistic regression framework to predict the risk of a patient's future health status such as death or illness [1, 2]. For example, in cardiology, models may be developed to predict the risk of having cardiovascular disease. Predictions based on these models are useful to both doctor and patient in making joint decision on future course of treatment. However, before using these models in risk prediction it is essential to assess their predictive performance using data other than that used to develop the models, which is termed as 'validation' [3, 4]. A good risk model is expected to demonstrate good calibration (accuracy of prediction) and discrimination (the ability of model to distinguish between low-and-high risk patients) in new dataset. A risk model that perform well in development data (that used to fit the model called 'training' set) may not perform similar to the validation data (that used to validate the model called 'test' set). One of the main reasons for not performing well in test data is model overfitting which causes too high prediction for high risk patients and too low for low risk patients. The overfitting occurs frequently when the number of events in training data is lower than the number of risk factors. After employing expert knowledge even if one fits the model with reduced the number of predictors, the ratio of the number of event to the number of predictors (EPV) often very low. However, as a rule of thumb, it has been suggested in literature that the risk model performs well when EPV is at least 10 [5]. Although the choice of this cut-off has some criticisms [6] for not being based on scientific reasoning except empirical evidence, it is found useful for quantifying the amount of information in the data relative to model complexity [7, 8]. However, the requirement of minimum EPV is often difficult to achieve when the risk models develop for low-dimensional data with rare outcome or small-and moderate-size, and for high-dimensional data where the number of predictors is usually higher than the number of sample observations.
To overcome the problem related to overfitting, some studies [9, 10] explored the use of penalized regression methods in risk prediction. Of them Ambler et al. [9] explored the use of two popular penalized regression methods, such as ridge [11] and lasso [12], in risk prediction for low-dimensional survival data with rare events and found that both methods improve calibration and discrimination compared with the ML based standard Cox models. Pavlou et al. [10] reviewed and evaluated ridge and lasso and their some extensions, such as elastic net, adaptive lasso etc [13–15], in risk prediction for low dimensional binary data with rare events and found that these methods can offers improvement, particularly for model overfitting, over the standard logistic regression model. Although these studies showed some improvement in risk prediction for rare-event data by using the penalized methods, there is no specific guidelines how risk prediction can be managed in the presence of separation, which frequently occur for such rare-event or sparse data. More specifically, the problem of separation, first reported by Albert and Anderson [16], is the case where one or more predictors have strong effects on response and hence (nearly) perfectly predict the outcome of interest. Table 1 presents an example of both complete (perfect prediction) and quazi-complete separation (nearly perfect prediction) caused by a dichotomous predictor X against binary outcome Y.
Table 1 Example of separation due to a dichotomous predictor X against outcome Y
Separation may occur even if the data is large but there is sufficient number of strong predictors. The likelihood of separation is higher for categorical predictors with rare category compared to the continuous predictor [17]. When developing model in the presence of separation, ML based logistic regression faces several problems [16, 18]. These includes lack of convergence of maximum likelihood and even if it converges it produces biased (sometimes infinite) estimate of the regression coefficient [17]. An alternative to the ML approach in this situation is Firth's penalized method [19]. This approach removes the first order term (O(n −1)) in the asymptotic bias expansion of the MLEs of the regression parameters by modifying the score equation with a penalty terms known as Jeffreys invariant prior. Heinze and Schemper [17] provided an application of Firth's method to the solution of the problem of separation in the logistic regression. Further the applications of Firth's method have been provided to proportional and conditional logistic regressions for situations with small-sample bias reduction and solution to problem of separation [20, 21].
However, one of the criticisms of Firth-type penalty in recent studies [22, 23] is that it depends on observed covariate data which can lead to artifacts such as estimates lying outside the range of prior median and the MLE (which is known as Bayesian non-collapsibility). An alternative to this, Greenland and Mansournia [22, 23] suggested logF(1,1) and logF(2,2) priors as default prior for logistic regression. As argued by the authors, the proposed logF priors are transparent, computationally simple, and reasonable for logistic regression. However, despite the attractive advantages of these penalized methods including Firth's method for sparse or small data sets, limited studies have been conducted to explore their use in risk prediction. This paper evaluates the predictive performance of these penalized methods for sparse data and compares the results with the ML based method and the other commonly used penalized method such as ridge. Although lasso is a commonly used method, it is popular for variable selection. Risk prediction and variable selection are different issues, and in this paper we have focused on prediction and hence excluded lasso.
This paper is organized as follows. The next section briefly describes all penalized methods under study. Then the following sections describe the simulation study, an illustration of the methodologies using stress ecocardiography data, and finally discussion and conclusions.
Maximum likelihood based logistic regression model
Let Y i ,(i=1,2,…,n), be a binary outcome (0/1) for the ith subject which follows Bernoulli distribution with the probability π i =Pr(Y i =1). The logistic regression model can be defined as
$$\begin{array}{*{20}l} \text{logit}[\pi_{i}|\boldsymbol{x}_{i})]=\eta_{i}=\boldsymbol{\beta}^{T}\boldsymbol{x}_{i}, \end{array} $$
where β T is a vector of regression coefficients of length (k+1), and x i is the ith row vector of the predictor matrix x which has order n×(k+1). The term η i =β T x i is called as risk score or 'prognostic index'.
In standard MLE, the model is fitted by maximizing the log likelihood denoted by l(β).
Penalized methods for logistic regression model
Whereas in penalized methods, l(β) is maximized subject to constraints on the values of regression coefficients. The constraints are fixed in such a way so that the regression coefficient shrinks towards zero in comparison with MLE, which may help to alleviate overfitting. More specifically, the penalized regression coefficient is obtained by maximizing the penalized log likelihood denoted by ł (β)−pen(β), where pen(β) is the 'penalty term'. The penalty term is the functional form of constraints. The penalized methods differ from each others in the choice of penalty term. The following subsection briefly discusses some popular penalized methods.
Firth's penalized method
In order to remove first order bias in MLEs of the regression coefficient, Firth [19] suggested to use penalty term \(\frac {1}{2}\text {trace}[I(\boldsymbol {\beta })^{-1}\partial I(\boldsymbol {\beta })/\partial \beta _{j}]\) in the ML based score equation U(β j )=∂ l(β)/∂ β j =0. The modified score equations are then U(β j )∗=U(β j )+1/2trace[I(β)−1 ∂ I(β)/∂ β j ]=0 (j=1,…,k), where I(β)−1 is the inverse of information matrix evaluated at β. The corresponding penalized log-likelihood function for the above modified score function is l(β)+1/2 log|I(β)|. The penalty term used above is known as Jeffreys invariant prior and its influence is asymptotically negligible. The Firth type penalized MLE of β is thus \(\hat {\boldsymbol {\beta }}=\text {argmax}\left \{ l(\boldsymbol {\beta })+1/2 \log |I(\boldsymbol {\beta })|\right \}\). This approach is known as bias preventive rather than corrective. However, Greenland and Mansournia [23] identified some problems in Jeffreys prior (equivalent to Firth's penalty term). These includes i)Jeffrey's prior is data-dependent and includes correlation between covariates ii) the marginal prior for a given β can change in opaque ways as model covariates are added or deleted, which may provide surprising results in sparse dataset, and iii) it is not clear how the penalty translate into prior probabilities for odds ratios.
Penalized method based on logF prior
To overcome these problems, Greenland and Mansournia [23] proposed a class of penalty functions pen(β)= ln(|I(β)|−m) indexed by m≥0, which produce MLE for m=0. Then the penalized log-likelihood is equal to l(β)+m β/2−m ln(1+e β). They showed that the antilog of the penalty term m β/2−m ln(1+e β) is proportional to a logF(m,m) density for β, which is the conjugate family for binomial logistic regression [24, 25]. It is noted that the prior degrees of freedom m in logF prior is exactly the number of observations added by the prior. Then the corresponding penalized ML estimate can be obtained as \(\hat {\boldsymbol {\beta }}=\text {argmax}\left \{l(\boldsymbol {\beta })+m\boldsymbol {\beta }/2-m\ln (1+e^{\boldsymbol {\beta }}) \right \}\). This shows that \(\hat {\boldsymbol {\beta }}\) has first order (O(n −1)) bias of zero for m=1, away from zero for m<1, and shrinks toward zero for m>1. This showed that F(0,0) is equivalent to MLE, and F(1,1) includes Jefrreys prior in one parameter model, for example, matched pair case-control. Greenland and Mansournia strongly argued against imposing a prior on the intercept to make sure that the mean predicted probability of binary condition is equal to the proportion of events. In this study, we focused on F(1,1) and F(2,2) prior for computational simplicity.
Ridge penalized method
Le Cessie and van Houwelingen [11] uses the penalty term as \(\lambda _{2}\sum _{j=1}^{k} \beta _{j}^{2}\), where λ 2 is a tuning parameter that modulates the trade-off between the likelihood term and the penalty term and is usually selected as data-driven procedure such as cross validation. The ridge log-likelihood is thus defined as \(l(\boldsymbol {\beta })-\lambda _{2}\sum _{j=1}^{k}\beta _{j}^{2}\) and hence \(\hat {\boldsymbol {\beta }}=\text {argmax}\left \{ l(\boldsymbol {\beta })-\lambda _{2}\sum _{j=1}^{k}\beta _{j}^{2} \right \}\). Ridge was initially developed to solve the problems due to multicolinearity. However, it shrinks the regression coefficient towards nearly zero and hence can be performed well to alleviate overfitting in risk prediction in the scenario with correlated predictors.
Evaluating predictive performance
Three common approaches to evaluate the predictive performance of a risk model [26]. These are i) calibration (the agreement between the observed and predicted risk in a group of subjects) ii) discrimination (the ability of model to distinguish between low-and high-risk patients) iii) overall prediction accuracy.
Calibration: We assessed calibration by calculating calibration slope, which can be obtained by re-fitting a binary logistic regression model with linear predictor or prognostic index (PI) derived from the original model as the only predictor. The estimated slope \(\hat {\beta }_{PI}\) is the calibration slope. If \(\hat {\beta }_{PI}=1\), it suggests perfect calibration; \(\hat {\beta }_{PI}<1\) suggests overfitting, and \(\hat {\beta }_{PI}>1\) suggest underfitting.
Discrimination: We assessed discriminating ability of the model by quantifying the area under receiver operating characteristic curve (AUC), graph of sensitivity (true-positive rate) versus one minus specificity (true-negative rate) evaluated at consecutive threshold values of the predicted risk score or probability derived from the model. Alternatively AUC can be obtained by quantifying the probability that, for a randomly selected pair of subjects, the subject who experienced the event of interest had higher predicted risk derived from the model than those without experiencing the event. A value AUC = 0.5 indicates no discrimination and 1 suggest perfect discrimination.
Overall predictive performance: The overall prediction accuracy is quantified using Brier score, which is the mean of the squared difference between the observed and predicted risk for each patient derived from the model. The lower the BS, the better the prediction of a model and BS=0 indicates perfect prediction. For ease of interpretation we reported root BS(rBS). In addition to the rBS, we also reported average predictive probability (APP) of the model to see how the predicted value differ from the corresponding observed value.
All the analyses and simulations were conducted in Stata version 12. Several Stata packages and functions were used to fit the models in different methods under study. These includes 'logit', 'firthlogit', 'penlogit', and 'plogit' along with 'plsearch' for MLE, FIRTH, logF and RIDGE, respectively. The calculation of calibration slope and Brier score were performed using self written Stata code and AUC using the package 'roctab'.
Example data: stress echocardiography data
The dataset used for simulation and illustration is in public domain and originally extracted from the study conducted by Krivokapich et al. [27] where the aim was to quantify the prognostic value of dobutamine stress echocardiography (DSE) in predicting cardiac events in 558 patients (male 220 and female 338) with known or suspected coronary artery disease. The responses of interest whether or not a patient suffered from either of 'death due to cardiac arrest', or ' myocardial infarction (MI)', or ' revascularization by percutaneous transluminal coronary angioplasty (PTCA)' or 'coronary artery bypass grafting surgery (CABG)' over the next year after having the test. There were 24 patients with cardiac death, 28 with MI, 27 with PTCA, 33 with CABG and 89 with any cardiac event (Cevent), which implies that the each of the events was rare. The main predictor of interest are age, history of hypertension (HT: yes/no) and diabetics mellitus (DM: yes/no), history of prior MI (yes/no) and PTCA (yes/no), status of DSE test (positive DSE:positive/negative), wall motion anamoly on echocardiogram (rest WMA:yes/no), ejection fraction on dobutamine(Dobutamine EF), and base ejection fraction (base EF).
The performance of the penalized methods in risk prediction over standard ML based logistic regression were investigated using a simulation study. We conducted simulation i) firstly to assess and compare the properties of the regression coefficients of the different methods (MLE, FIRTH, logF(1,1), logF(2,2), RIDGE) under study and ii) secondly to assess and compare the predictive performance between the methods.
Assessing the properties of the regression coefficients
To assess the properties of the regression coefficients such as bias and mean squared error (MSE), we generated two independent predictors of which one is continuous (X 1) generated from standard normal and the other is dichotomous (X 2) generated from Bernoulli distribution with 50% events. We then generated binary response from Bernoulli distribution with probability π i (i=1,…,n) calculated from true logistic model logit(π)=β 0+β 1 X 1+β 2 X 2, where β 1=0.30 and β 2=0.9. With this combination, the binary covariate created separation for some of the simulated datasets particularly with low prevalence. The value of β 0 vary to generate data with varying level of prevalence. The scenarios were created by varying the prevalence, on an average, (p) as 5.5, 11.5 20.4 and 39.6% for a fixed sample size n=120. For each scenario, 1000 datasets were generated and all regression approaches under study were fitted to each dataset. When fitting RIDGE the respective tuning parameters were selected through 10-fold cross validation. The estimates of the regression coefficients of the respective models were obtained as the mean over the number of simulations where convergence achieved. Noted that only MLE and RIDGE were failed to converge (due to low prevalence or separation or both) in some datasets, and the maximum failure rate for MLE and RIDGE were 13 and 51%, respectively for the lowest prevalence scenario. The failure rate decreases as the prevalence increases. Finally the relative bias (%) and mean squared error (MSE) of the estimates were reported and compared if the performance vary across the scenarios.
The results in Table 2 showed that the RIDGE estimator, in general, provides the highest amount of relative bias (%), which is followed by the MLE and logF(2,2) whereas FIRTH and logF(2,2) provides negligible bias. For the coefficient of the dichotomous predictor (β 2) logF(2,2) provides more bias compared to those for continuous predictor (β 1). The amount of bias, in general, is the highest for the low prevalence data and the lowest for the high prevalence data. However, the RIDGE, in general, produces the lowest MSE, and the highest MSE is produced by the MLE for β 1 and by FIRTH for β 2. The amount of MSE, in general, decreases with the increasing prevalence.
Table 2 Performance of the estimated regression coefficients of models fitted using different methods under study
Assessing the predictive performance
To assess the predictive performance of the methods, we conducted two simulation series following the simulation design in Pavlou et al. [10] used for similar type of study. The first simulation series is based on the real stress echocardiography data where only responses were generated and in the second simulation series we generated both covariates and responses.
Stress echocardiography simulation
In the first simulation series based on real data, we simulated data and evaluated the predictive performance of the models for different EPV scenarios using the following steps:
Fit the following logistic regression model for the response "any cardiac event" with Firth's penalized method (to avoid bias in the estimate of the regression coefficient) to obtain the true model:
$$\begin{array}{*{20}l} {}\text{logit(Pr(Cevent=1))}&=\beta_{0}+\beta_{1}{dobef}+ \beta_{2} wma +\beta_{3} posse\\&\quad+\beta_{4} bsef+\beta_{5} ht+\beta_{6} age \end{array} $$
To create a training data, choose the EPV and prevalence (prev), and then calculate sample size for the respective EPV given the number of predictors p as \(n=\frac {EPV \times p}{prev}\). Sample with replacement the n values of the covariates in the true model from original data. For each of the n values of the covariates, simulate new responses from Bernoulli distribution with the probability calculated from the fitted model. However, replace the value of β 0 by -0.65 to confirm the prevalence of the response (prev), on an average, 15.5% for all EPV scenarios.
With this combination, check and record if separation occurred due to any of the binary covariates ('posse' or 'wma', or 'ht' or combination of them). Otherwise to create separation, enlarge the true value of the respective coefficient of the binary covariate to some extents. Note that the chances of separation is expected to increase with decreasing EPV value.
To create a test dataset, sample with replacement m×n (m times of the original data of size n=558, we considered m=2) values of the covariates. Then simulate the corresponding new responses from the same true model used for simulating training data.
Repeat the steps (ii)-(iii) to produce 1000 training and 1000 test datasets.
Fit the risk models (using MLE and all types of penalized regression methods under study) to each of the training data sets and check whether convergence was achieved. Then evaluate their predictive performance (if convergence achieved) by means of calibration slope, AUC, root Brier score, and average predictive probability (APP) using the corresponding test dataset. Summarize the predictive performance over the number of simulations for which convergence is achieved.
The predictive performance of all regression methods was investigated against EPV=2, 3, 5, 10 to see if the performance vary across the scenarios. When the predictive performance against EPV was assessed by means of calibration slope, the MLE showed poor performance by producing overfitted model (calibration slope substantially lower than 1) for EPV=2, 3, 5 (Fig. 1). All penalized methods offered improvement to some extents except the RIDGE which produced underfitted model (the average value of the calibration slope greater than 1 with high SD). In addition, the RIDGE failed to converge for the maximum 8.4% of the simulations particularly when EPV=2. Almost equal improvement was offered by the Firth-type and both the logF(1,1) and logF(2,2) penalized methods. In general all methods including MLE showed almost equal performance in terms of calibration for high EPV (EPV=10). When the predictive performance (discriminatory ability) was assessed through AUC, all penalized methods showed better performance with greater AUC than MLE for the low EPV scenarios (Fig. 2). Of them the RIDGE provided highest AUC value. However, the amount of improvement in the discrimination, in general, was comparatively lower than that for calibration. All methods perform almost equally for high EPV (EPV=10). Similarly the penalized methods offered improvement in the overall predictive performance for individual prediction assessed through rBS to some extents for low EPV (Fig. 3). Of them, the RIDGE offered greater improvement. However, for low EPV while both the logF(1,1) and logF(2,2) penalized methods provided accurate estimate of the true average predicted probability (APP) 15.2%, the FIRTH-type penalized method overestimate the true value. The amount of bias in FIRTH-type estimate is even larger than that produced by MLE and RIDGE (Fig. 4).
Performance of the methods was assessed using calibration slope and compared. Results were summarized over the number of simulations for which convergence is achieved. The maximum failure rate of convergence for RIDGE, out of 1000 simulations, is 8.4% when EPV=2. The values outside the whisker were not plotted to make the plot readable. The horizontal dash line is the median calibration slope for MLE and the solide line is the optimal value
Performance of the methods was assessed using area under ROC (AUC) and compared. Results were summarized over the number of simulations for which convergence is achieved. The maximum failure rate of convergence for RIDGE, out of 1000 simulations, is 8.4% when EPV=2. The horizontal solide line is the median AUC for MLE
Performance of the methods was assessed using root Brier score (rBS) and compared. Results were summarized over the number of simulations for which convergence is achieved. The maximum failure rate of convergence for RIDGE, out of 1000 simulations, is 8.4% when EPV=2. The horizontal solide line is the median rBS for MLE
Performance of the methods was assessed using Average Predicted Probability (APP) and compared. Results were summarized over the number of simulations for which convergence is achieved. The maximum failure rate of convergence for RIDGE, out of 1000 simulations, is 8.4% when EPV=2. The horizontal solide line is the observed APP
Further simulation
In the second simulation series with the same EPV scenarios, we simulated both covariates and response under two predictive models, one with weak predictive ability and the other with strong predictive ability, using the following steps:
For creating training data, choose the EPV and prevalence (prev) and calculate sample size (n) for the given EPV value and the number of predictors using the same formula previously used.
For each observation in training data, first simulate three continuous predictors (X 1,X 2,X 3) independently from standard normal distribution and two binary predictors (X 4,X 5) independently from Bernoulli distribution one with low (20%) and the other with high (60%) prevalence.
Simulate the corresponding responses from Bernoulli dis-tribution with probability calculated from the true model:
$$\begin{array}{*{20}l}{}\text{logit}(\pi)=\beta_{0}+\beta_{1} X_{1} +\beta_{2} X_{2}+ \beta_{3} X_{3}+\beta_{4} X_{4}+\beta_{5}X_{5}. \end{array} $$
For the model with weak predictive ability, the values of the regression coefficient were set as β 0=−1.5β 1=0.2,β 2=0.5β 3=−0.03,β 4=0.05 and β 5=−0.6 and for the model with strong predictive ability, the corresponding true values were set as β 0=−3.5,β 1=1.2,β 2=−0.9β 3=0.9,β 4=1.2 and β 5=1.2. In each case, the value of the β 0 confirms the desired prevalence of the response. With this combination, the binary covarites X 5 in the model with weak predictive ability and X 4 in the model with strong predictive ability create separation in some of the simulations. Check and record if separation occurred.
Create test data with size 1000 (much larger than the training data) for the similar level of EPV and prevalence. For each observation in the test data, simulate the same predictors as in the test data and the corresponding response from the same true model.
Repeat the steps (ii)-(iv) to produce 1000 training and 1000 test datasets
Fit risk models (using all methods) using training data, count if convergence was achieved for the respective model, and evaluate their predictive performance (if convergence was achieved in training data) using test data as before. Finally summarize the predictive performance over the number of simulations for which convergence is achieved.
The results revealed that, for both predictive models (weak and strong predictive abilities), all the penalized methods offered improvement in calibration over MLE for low EPV, except for the RIDGE which in turn provided underfitted model (calibration slope grater than 1 with high SD) (Table 3). The amount of improvement by the other penalized methods was almost equal. However, all the penalized methods except the RIDGE offered negligible improvement in the discrimination for low EPV. Similarly all the penalized methods showed improvement to some extents in the overall predictive performance by lowering the rBS value compared to that for MLE. For both predictive model, the average predicted probability (APP) estimated by the both the logF(1,1) and logF(2,2) were almost equal to the average observed probability, however the Firth-type penalized method introduced positive bias in the estimate of the average probability. The amount of bias was even larger than that for MLE and RIDGE. In case of both models, the maximum failure of convergence (due to separation or low EPV or both) was reported for RIDGE.
Table 3 Performance measures for the model s with both weak and strong predictive ability. Results were summarized over the number of simulations for which convergence is achieved. The maximum failure rate of convergence for RIDGE with weak predictive ability, out of 1000 simulations, is 40% for the lowest EPV
Illustration using stress echocardiography data
The aim is to derive risk models using different penalized methods discussed earlier and the standard MLE to predict the risk of having a cardiac event and then to evaluate and compare their predictive performance. We fitted separate models for predicting the risk of each of the four cardiac events and a model for the risk of any of the events using each regression approaches; that is, a total of five models for each of the binary events were fitted using six different regression methods under study and altogether 25 models for all five binary responses.
The models were fitted using training data (contains 60% of total data randomly selected) and their predictive performance were evaluated using test data (contains rest of 40%). The associated predictors for each cardiac event were selected based on the information from literature and results of likelihood ratio test (LRT). Different combinations of predictors were tested using LRT to come up with a final model for each cardiac event. Then the same model was then fitted in training data using six different methods. Note that quasi-complete separation due to binary predictors in training data was identified for the responses 'PTCA' and ' cardiac death', and hence, in case of convergence failure for RIDGE or MLE, the estimates reported are based on the last iteration. The estimated coefficients of the respective model are then summarized in Table 4. For all types of response, the estimated regression coefficients for MLE is larger than all penalized methods. Because all the methods shrink the coefficient towards zero. The amount of shrinking was higher for the RIDGE in the most of the cases. However, the main purpose here is to evaluate the predictive performance of the methods rather than comparing their estimated regression coefficients. The predictive performance of all models were then evaluated using test data, and the results were summarized in Table 5.
Table 4 Modeling the risk of cardiac events. Estimate of the regression coefficients with SE in the parenthesis
Table 5 Performance of penalized methods in predicting cardiac events
It is observed from results in Table 5 that all models faced the problem of overfitting (calibration slope <<1) particularly for those response for which the EPV is low (EPV <10). The amount of overfitting is lower for all penalized methods compared to MLE. In terms of discrimination all methods including MLE provided comparable results. For all types of response, the greater improvement was observed in the calibration (calibration slope) compared to those in both discrimination (AUC) and overall performance (BS). Firth methods produced higher value of the average predicted probability (APP) for all type of responses.
The probable reason for producing overfitted models (very low value of the calibration slope) even for the penalized methods is that the size of the test data and particularly the number of events for all types of response were very small compared to the number of events (approximately 100) required for correct estimation of the predictive accuracy measures [28]. Therefore, further the predictive performance of all models were evaluated in test data consisting of larger sample size and number of events compared to the previous test data. This was created by expanding 5 times the original (previous) test data so that the required number of events is achieved. In this procedure each subject replaced his/her information for the other 4 subjects. The results showed that calibration slope was comparatively more closer to 1 (suggesting improvement in calibration) for the penalized methods for all types of responses, particularly for which EPV was high (results not showed). Similar results were obtained for the AUC and Brier score for all types of models of all responses.
Penalized regression methods (such as RIDGE and LASSO) has increasingly being used for developing models for high dimensional data where the number of predictors is higher than the number of subjects. Furthermore several studies [29, 30] have also been conducted to make relative comparison between the methods for high dimensional case and found that RIDGE performed well when data have highly correlated predictors and LASSO performed well when variable selection is required. Although few studies [9, 10] evaluated RIDGE, LASSO and others in risk prediction for low-dimensional survival and binary data with few events, however, they often ignored Firth-and logF-type (such as logF(1,1) and logF(2,2)) penalized methods, despite their attractive advantages in reducing finite sample bias in the estimated regression coefficient and solving problem of separation that commonly occurs in low-dimensional small or sparse datasets. This paper explored the use of these methods in risk prediction for small and sparse data and compared their predictive performance with MLE and the other penalized method (RIDGE). In particular we focused on comparing the predictive performance of the methods through assessing calibration, discrimination and overall predictive performance when EPV is less than 10 in low-dimensional setting.
The results from simulation studies and illustration with real data revealed that while the MLE produced overfitted model with poor predictive performance (in terms of calibration), all penalized methods offered some improvements except for the RIDGE which in turn produced underfitted models (calibration slope greater than 1 with large variability). All other penalized methods (Firth-type and both logF(1,1) and logF(2,2)) offered similar amount of improvement in calibration. However, the improvement in the discrimination in general was lower than that in calibration. The reason can be explained similarly with Pavlou et al. [10] as that the penalized methods tend to shrink the predicted probability towards the average compared with the MLE and hence the ordering of the predicted probabilities with and without experiencing the event in most patient pairs tends to remain unchanged after shringkage, which resulted in small improvement in AUC values of the penalized methods over MLE. All the penalized methods offered some improvement in the overall predictive performance (lower BS compared to those with MLE). Although all penalized methods correctly estimate the average predicted probability, Firth-type penalization introduced bias. The findings are similar to what obtained in other studies [10] that explored the use of some penalized methods such as ridge, lasso etc in risk predictions for low-dimensional data.
Based on the findings of the study it can be recommended to use logF-type penalized method instead of MLE in risk prediction for low dimensional data small or sparse data. Because firstly this approach showed minimum bias in the estimate of regression coefficient and greater improvement in predictive performance than MLE, particularly in calibration by removing the amount of overfitting to some extents. Secondly, this approach has some additional advantage particularly for solving the problems due to separation. Of the two types of logF penalization, logF(1,1) is preferable to logF(2,2) because logF(2,2) though provides similar predictive performance but produces some bias in the regression coefficient particularly for the dichotomous covariates. Although the Firth-type penalized method have great advantage for solving the problems related to separation and showed comparable results with the logF-type penalized methods with respect to calibration, discrimination and overall predictive performance, it produced bias in the estimate of the average predicted probability. The reason is that Firth's approach imposes prior on the intercept (which control the average predicted probability) and as a result it shrink the average predicted probability towards 0.5 and hence produced upward bias in the average predicted probability. However, the logF make the intercept free from the penalization and hence correctly estimates the average predicted probability. Similarly although RIDGE showed greater improvement in the discrimination and the overall predictive performance, it often provides under-fitted model. The striking disadvantages of RIDGE is that it has frequent convergence failure for data with low EPV or if there is separation. The rate was high (even higher than MLE) if data have combination of both low EPV and separation. This finding is similar to those [31] which reported low EPV or separation or combination of both as one of the reasons for the convergence-failure in RIDGE, although other studies [32] reported it as wrong choice (small value) of tuning parameter.
In the presence of separation, developing a risk model using any other penalized methods, except for the Firth-type and logF-type methods, under study is challenging. Because RIDGE and LASSO-type penalized methods were originally developed particularly either for shrinking the regression coefficient or variable selection in high dimensional data rather than solving separation problem. However, the main limitation of logF type penalized approach is that it cannot be used directly for variable selection. If small-to moderate-level of variable selection is required in low-dimensional data with sufficient number of predictors, logF method can also be used in risk prediction after selecting important predictors using results from exploratory analysis of the data and likelihood ratio test conducted in different combinations of nested models.
This study did not focus on the use of Firth-type and logF-type penalized method in risk prediction for low-dimensional survival data with few events where standard Cox regression is reported to be unreliable [33]. Further research may be possible to evaluate the predictive performance of these methods in comparison with the standard Cox model and the other penalized methods.
Average predicted probability
AUC:
Area under receiver operating characteristic curve
EPV:
Event per variable
FIRTH:
Firth's penalized method
MLE:
Maximum likelihood estimation
Abu-Hanna A, Lucas PJF. Prognostic models in medicine. Methods Inform Med. 2001; 40:1–5.
Moons KGM, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why and how?. BMJ. 2009a; 338:1317–20.
Altman DG, Royston P. What do you mean by validating a prognostic model?. Stat Med. 2000; 19:453–73.
Moons KGM, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009b; 338:1487–90.
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996; 49:1373–9.
Moons KG, de Groot JA, Linnet K, Reitsma JB, Bossuyt PM. Quantifying the added value of a diagnostic test or marker. Clin Chem. 2012; 58(10):1408–17.
Bouwmeester W, Zuithoff N, Mallett S, Geerlings MI, Vergouwe Y, Steyerberg EW, Altman DG, Moons KGM. Reporting and methods in clinical prediction research: a systematic review. PLOS Medecine. 2012; 9(5):e1001221.
Steyerberg EW, Eijkemans MJC, Harrell FE, Habbema JDF. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med. 2000; 19(8):1059–79.
Ambler G, Seaman S, Omar RZ. An evaluation of penalised survival methods for developing prognostic models with rare events. Stat Med. 2012; 31(11–12, SI):1150–61.
Pavlou M, Ambler G, Seaman S, De Iorio M, RZ O. Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events. Stat Med. 2016; 35(7):1159–77.
Cessie SL, van Houwelingen JC. Ridge estimators in logistic regression. J R Stat Soc Series C. 1992; 41(1):191–201.
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Series B. 1996; 58:267–88.
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B. 2005; 67(2):301–20.
Zou H. The adaptive lasso and its oracle properties. J Am Stat Assoc. 2006; 101(476):1418–29.
Zou H, Zhang HH. On the adaptive elastic-net with a diverging number of parameters. Ann Stat. 2009; 37(4):1733–51.
Albert A, Anderson JA. On the existence of maximum likelihood estimates in logistic regression models. Biometrika. 1984; 71(1):1–10.
Heinze G, Scemper M. A solution to the problem of separation in logistic regression. Stat Med. 2002; 21(16):2409–19.
Schaefer RL. Bias correction in maximum likelihood logistic regression. Stat Med. 1983; 2:71–8.
Firth D. Bias reduction of maximum likelihood estimates. Biomertika. 1993; 80:27–38.
Greenland S, Schwartzbaum JA, Finkle WD. Problems due to small samples and sparse data in conditional logistic regression analysis. Am J Epidemiol. 2000; 151(5):531–9.
Lipsitz SR, Fitzmaurice G, Regenbogen SE, Sinha D, Ibrahim JG, Gawande AA. Bias correction for the proportional odds logistic regression model with application to a study of surgical complications. J R Stat Soc Series C. 2013; 62(2):233–50.
Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016; 352:i1981.
Greenland S, Mansournia MA. Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions. Stat Med. 2015; 34(23):3133–43.
Greenland S. Prior data for non-normal priors. Stat Med. 2007; 26:3578–90.
Greenland S. Generalized conjugate priors for bayesian analysis of risk and survival regressions. Biometrics. 2003; 59:92–9.
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2009; 21(1):128–38.
Krivokapich J, Child J, Walter DO, Garfinkel A. Prognostic value of dobutamine stress echocardiography in predicting cardiac events in patients with known or suspected coronary artery disease. J Am Coll Cardiol. 1999; 33(3):708–16.
Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016; 35(2):214–26.
Benner A, Zucknick M, Hielscher T, Ittrich C, Mansmann U. High-dimensional cox models: the choice of penalty as part of the model building process. Biometrical J. 2010; 52:50–69.
van Wieringen WN, Kun D, Hampel AL R, Boulesteix. Survival prediction using gene expression data: a review and comparison. Comput Stat Data Anal. 2009; 53:1590–603.
Shen J, Gao S. A solution to separation and multicollinearity in multiple logistic regression. J Data Sci. 2008; 6(4):515–31.
Goeman J, Meijer R, Chaturvedi N. L1 and L2 penalized regression models. R Package version 0.9-47; 2016. Retrieved from http://CRAN.R-project.org/package=penalized.
Ojeda FM, Müller C, D B, A TD, Schillert A, Heinig M, Zeller T, Schnabel RB. Comparison of cox model methods in a low-dimensional setting with few events. Genomics Proteomics Bioinforma. 2016; 14(4):235–43.
The authors acknowledge Alan Garfinkel and Frank Harrell for making available the dataset used in this study in a public domain under the department of biostatistics, Vanderbilt University, USA. In addition the authors thankful to associate editors and the reviewers for the valuabale suggestion and comments which strengthen the quality of the paper.
The authors received no specific fund for this study. They did this research based on their on interest and used data from secondary source.
The dataset used in this study can be downloaded freely from a public domain at http://biostat.mc.vanderbilt.edu/DataSetsunder the authority of the department of biostatistics, Vanderbilt University, USA.
MSR contributed to the design of the study; MSR and MS analyzed the data; MSR and MS wrote the manuscript. All authors have read and approved the final manuscript.
The authors declared that they have no competing interest.
As the dataset is freely available in a public domain at http://biostat.mc.vanderbilt.edu/DataSets and is permitted to use in research publication, the ethics approval and consent statement has been approved by the authority who made the data available for public use.
Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh
M. Shafiqur Rahman & Mahbuba Sultana
M. Shafiqur Rahman
Mahbuba Sultana
Correspondence to M. Shafiqur Rahman.
Rahman, M.S., Sultana, M. Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data. BMC Med Res Methodol 17, 33 (2017). https://doi.org/10.1186/s12874-017-0313-9
Received: 25 July 2016
Prediction model
Overfitting


text:
Taxation Ruling
Leasing arrangements of plant and machinery
may be releasedFOI number: I 1101973
A former Commissioner of Taxation issued a statement in reply to a request for advice on the likely taxation implications arising from the practice of finance companies leasing plant on a rental basis as opposed to the normal hire purchase arrangements
2. The statement is reproduced below.
Income tax: leasing arrangements of plant and machinery statement by the Commissioner of Taxation, Sir Patrick McGovern, CBE
3. Some twelve months ago a copy of an agreement providing for the leasing of plant and machinery was submitted to the Taxation Office for a ruling whether or not payments under the agreement were allowable deductions for income tax purposes. As the document was a lease agreement simpliciter, the enquirer was advised that the rentals payable would be deductible so long as the leased goods were used by the lessee in the production of his assessable income.
4. Subsequent investigation disclosed that, in most transactions of this nature, there were auxiliary arrangements under which, on the expiration of the lease, the lessee would gain ownership, or retain the use, of the goods previously leased by him. In some cases it was found that a three-party arrangement was effected whereby the lessor agreed, either in subsidiary documents or in correspondence, to sell the goods, on the expiration of the lease, to an intermediary who in turn agreed to sell them to the lessee. In other cases the circumstances surrounding the transaction were such as to establish the existence of oral commitments.
5. Some publicity material promoting leasing arrangements of plant and machinery made a feature of the opportunity given to the lessee of acquiring the goods at a low written-down value. It was stated further that the new lease procedure had the effect of reducing the amount of tax payable by the person acquiring the goods and thus had advantages over conventional hire-purchase.
6. In view of such statements, I considered it expedient last February to issue a warning to persons concerned in this type of transaction that it might be necessary to ascertain the substance of the transaction between the parties from all the covenants entered into, whether expressed in the lease agreement or otherwise.
7. In short, it was made clear that, in determining the application of income tax legislation to this class of transaction, it was necessary to decide whether the payments were lease rentals or whether they were, in substance, consideration for the sale of the goods purported to be leased. In the latter case, of course, the payments would be outgoings of a capital nature which would not be deductible for income tax purposes.
8. Among the relevant factors which would determine this question are the following:-
the existence of any agreement, express or implied, and whether in the lease agreement or in subsidiary documents or correspondence, under which the property in the goods would pass from the lessor to the lessee; and
the degree of relativity between the "appraisal value" or "residual value" - by reference to which the amount of lease rentals was frequently determined - and the reasonable commercial value of the goods at the expiry date of the lease.
9. Further, where an arrangement had as one of its objects the purpose or effect of altering the incidence of income tax, a prima facie case for the application of section 260 of the Income Tax and Social Services Contribution Assessment Act existed. For this reason it would be necessary to examine each transaction in the light of its facts before any decision could be given.
10. This is the traditional approach of administrations not only in Australia but throughout the world. It arises from the difficulty of formulating general rules for application to particular facts.
11. Nevertheless, I have been pressed on all sides to delineate an area in which traders who wish to follow the procedure of leasing plant and machinery for reasons not connected with income tax saving may operate without fear of long drawn out legal argument after the event.
12. People making these representations maintain that it is not too much to expect from an administration that it should indicate the boundaries within which traders may operate without risk of subsequent challenge, entailing great disruption of business and possible monetary loss.
13. This is persuasive argument and, with pre-knowledge of the difficulties of formulating general principles for application to particular cases and without in any sense attempting to define the limits of construction that may be placed upon the relevant provisions of the income tax legislation, I have attempted in this instance to promulgate minimum conditions that would ensure to a person complying with them freedom from challenge from my administration.
14. As I have already indicated, the basic question for decision is whether the transaction is, in practical effect, an ordinary commercial lease entered into in the normal course of trade. Obviously, not all payments under agreements purporting to be lease agreements can properly be regarded as rentals to be allowed as deductions for income tax purposes.
15. If, for instance, the arrangement were such as to confer on the lessee, if he chose to avail himself of the option, a right whereby the property in the goods would pass to him from the lessor at any point of time, the arrangement would, in my opinion, constitute for all practical purposes a contract for the sale of the goods. Similarly, I should not regard as a normal commercial lease an arrangement under which, on the termination of the lease or any extension thereof, the lessee was permitted or enabled to retain the use of the goods - as, for example, through the property in the goods passing to his nominee or agent. In these contexts, it is considered to be immaterial whether the lessee's right to secure the property in (or the use of) the goods was conferred in the head agreement or in some collateral agreement or agreements.
16. The inclusion in the agreement or agreements of a provision that the leased goods be disposed of, at the termination of the lease, otherwise than by way of public auction raises a presumption that the lessee has rights of purchase. Such agreements do not satisfy the minimum conditions herein being defined.
17. This is not intended, of course, as restricting or confining an owner's rights to dispose of his goods in any way he may choose after the leasing is completed. The objection is against provisions under which, prior to the termination of the leasing period, arrangements are entered into for the disposal of the goods other than by public auction.
18. Another factor that would be regarded as inconsistent with a finding that the transaction was a normal commercial lease would be the inclusion in the leasing agreement of a provision under which, in the event of the sale price of the goods falling short of an agreed residual value, the shortage should be paid by way of adjustment by the lessee to the lessor.
19. An unreal or nominal residual value in leases of relatively short term e.g. up to 5 years, would, I consider, raise a strong presumption that the transaction was something more than an ordinary commercial lease. By this it is not intended to convey that the residual value must necessarily correspond with the depreciated value of the goods for income tax purposes but it should, in my view, be in conforming with some generally accepted basis of commercial or industrial valuation.
20. The following table has been prepared as indicating what I would regard, in this context, as minimum residual values for various categories of plant and machinery (classified according to depreciation rates on a prime cost basis for income tax purposes) at the end of leases ranging from one to five years:-
MINIMUM RESIDUAL VALUES - PERCENTAGE OF COST
Plant and Machinery Classified According to Income Tax Prime Cost Depreciation Rates
20% 15% 10% 7.5% 5%
1st year 60% 63.75% 67.5% 68.5% 70%
2nd year 45% 52.50% 60.0% 62.5% 65%
3rd year 30% 41.25% 52.5% 55.0% 60%
4th year 15% 30.00% 45.0% 50.0% 55%
5th year nil 18.75% 37.5% 45.0% 50%
21. Subject to what I have already said concerning express or implied arrangements for the transfer of the property to or the use of the property by the lessee, I would not seek to impugn a lease agreement for reasons specifically relating to appraisal or residual values if those values equalled or exceeded the minimum limits indicated in the foregoing table.
NO J146/569 P2 F192
Date original memo issued:
leased property
residual values
ITAA 260

redpajama_set_name: RedPajamaCommonCrawl


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Sustainable and ESG assets represent a growing share of portfolios for both asset owners and managers. But most investors remain unaware. A survey from BNY Mellon's Newton Investment Management found that 55% of American investors are unfamiliar with "social investment," including ESG and the broader category of responsible investing. Rising risks may serve to increase investor awareness. Evolving consumer attitudes, technology disruption and shortfalls in productive reinvestment represent rising "relationship risks" that can undermine a company's societal license to operate, according to a report from the $192 billion Lazard Asset Management. "We believe the breakdown of these relationships represent a threat to long-term fundamentals and ultimately returns." Last week, the global consultancy Mercer called out the possibility of sudden climate-risk "repricing events" that could upend portfolio values. BlackRock suggested investors are discounting climate risks lurking in municipal bonds, commercial real estate and U.S. utility stocks. Are your assets at risk?
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text:
Home செய்திகள் Sri Lanka and India hold military talks
Sri Lanka and India hold military talks
Sri Lanka and India held military level discussions on Tuesday in order to strengthen ties between the armies of both countries, the Sri Lanka army media unit said. The India-Sri Lanka Army to Army Staff Talks (AAST) was attended by an Indian delegation including Major General Gurdeep Singh of the Indian army and Colonel Vivek Tyagi, the Director of Military Training of the Indian Army. The meeting comes even as politicians in India's southern state of Tamil Nadu continued to raise objections to India providing military training to Sri Lanka. Tamil Nadu Chief Minister Jayalalitha Jeyaram and former Chief Minister M. Karunannidhi had on Tuesday demanded that the Indian government discontinue with the training for the Sri Lanka army. Tamil Nadu has been demanding an investigation into alleged war crimes committed during the final stages of the 30 year war between the Sri Lanka army and the Tamil Tiger rebels. However the Indian government had said it will continue with the training for the Sri Lankan military despite the protests. Following the AAST meeting both Sri Lanka and India are expected to sign the final minutes as an endorsement of the mutual agreement for implementation of the conclusions in the three-day deliberations. The Sri Lanka army media unit also said that the visiting Indian army delegation had separately met the Sri Lanka Army Commander Lieutenant General Jagath Jayasuriya on Tuesday. The AAST held on Tuesday was the second such meeting with the first held in India last year.]]>
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text:
EuroLeague - Round 27 MVP: Michael Eric, Darussafaka Tekfen Istanbul
21.03.2019 14:52 of Redazione Pianetabasket.com	see readings
Wednesday was a night to remember for Darussafaka Tekfen Istanbul center Michael Eric, who had the best game of his Turkish Airlines EuroLeague career in leading his team to victory and capturing MVP honors for Round 27. Eric came on late to spark Darussafaka past Khimki Moscow Region 91-85. He posted personal bests of 22 points, 15 rebounds and a performance index rating of 33, which was the best among winning teams in the round. Eric made 10 of 12 shots and also delivered 2 assists as Darussafaka snapped a six-game losing streak. He became the second Darussafaka player to be named MVP of the round after Brad Wanamaker, who achieved the feat in Round 29 of the 2016-17 campaign. The top performance index rating of the round came from Mike James, however, his AX Armani Exchange Olimpia Milan fell 92-89 at Real Madrid. James racked up a career-high PIR of 37 with 35 points and 8 rebounds. Eric's teammate Jeremy Evans had the third-best PIR of the round with 29. Evans went for 20 points, 8 rebounds and 2 assists. Rounding out the list of Round 27's best performers are the Khimki duo of Alexey Shved and Jordan Mickey with PIRs of 28 and 27, respectively. Shved went for 31 points and Mickey posted 22 points on near-perfect shooting in their defeat.
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EuroLeague - Round 24 MVP: Ante Tomic, FC Barcelona Lassa


text:
@class NSURL;

typedef NS_ENUM(NSUInteger, ALAssetRepresentationErrorCode) { _ALAssetRepresentationErrorCodeFileAccessError = 100 };

@interface ALAssetRepresentation ()
- (instancetype)_initWithAssetURL:(NSURL*)url localPath:(NSString*)path error:(NSError**)error;
@end

redpajama_set_name: RedPajamaGithub


text:
Irish Historical and Romantic Tales
« previous page | contents | next page »
Sculpture on Window, Cathedral Church, Glendalough: Beranger 1779 (From Petrie's Round Towers)
HISTORICAL AND ROMANTIC TALES
SECTION 1. Classes, Lists, and Numbers.
VEN from the most remote times, beyond the ken of history, the Irish people, like those of other countries, had stories, which, before the introduction of the art of writing, were transmitted orally, and modified, improved, and enlarged as time went on, by successive shanachies, or 'storytellers.' They began to be written down when writing became general: and a careful examination of their structure, and of the language in which they are written, has led to the conclusion that the main tales assumed their present forms in the seventh, eighth, and ninth centuries; while the originals from which they sprang are much older. Once they began to be written down, a great body of romantic and historical written literature rapidly accumulated, consisting chiefly of prose tales. Of many of the tales we have, in the Book of the Dun Cow, and the Book of Leinster, copies made in the eleventh and twelfth centuries; and there are numerous others in manuscripts copied by various scribes from that period to the present century, many of them from original volumes older than the Book of the Dun Cow.
In the Book of Leinster there is a very interesting List of the classes to which the ancient historical tales belong, with a number of individual tales named under each class as examples, numbering altogether 187. This List is as follows (with a few additions from other sources):—1. Battles: 2. Imrama, Navigations, or Voyages: 8. Tragedies: 4. Adventures: 5. Cattle-raids: 6. Hostings or Military Expeditions: 7. Courtships: 8. Elopements: 9. Caves or Hidings (i.e. adventures of persons hiding for some reason in caves or other remote places): 10. Destructions (of palaces, &c.): 11. Sieges or Encampments: 12. Feasts: 13. Slaughters: 14. Pursuits: 15. Visions: 16. Exiles or Banishments: 17. Lake Eruptions.
We have in our old books stories belonging to every one of these classes. The whole number now existing in MSS. is close on 600: of which about 150 have been published and translated. But outside these, great numbers have been lost: destroyed during the Danish and Anglo-Norman wars.


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text:
When Heidi Showed Up After All
C.T. May
Fiction: A symbolic interlude out on the stoop.
"She's a smart girl," I said. "And you like her, of course."
Akshay looked off toward the streetlight. "Sometimes I am not so happy," he said. "This girl has a habit of trifling with me."
"She's very smart. Neuroscience." I was pursuing my thoughts. "That's a tough field," I said. "Political science is okay too." Akshay was a poli-sci major. "She's got a good understanding of neuroscience," I continued. Heidi's grades were a good deal better than his.
Akshay turned and looked at me, wet-eye, baleful. His dark profile hung in the light. I pretended not to see. I gazed at the patch that the street light laid in the dark.
"It's not something I learned in a long life," I said. "Not something I've encountered in my decades of experience. I'm not a neuroscience person. But this is what I've heard: neuroscience is tough."
A girl walked on the other side of the street; her legs flashed white as she passed out of the shadows and back in. It wasn't Heidi.
"Goleman's book?" I said. "Daniel Goleman? That's my neuroscience. That's all I got. And I didn't read it that much."
Akshay was still looking at me.
"A couple of chapters," I said. "The, uh, flyleaf. What was written in the flyleaf. I read that."
He gulped out some sort of curse. It was his language, but I got the idea.
"She's a smart girl," I said. "I think she kind of figured it out. She wanted me to see."
Akshay stood up. The legs of his red tracksuit unfolded; the white piping straightened out. He was much taller than me. "Shut up," he said.
I began to find it all funny. "She has many sides," I told him. "Not an innocent girl. Really…"
His fist swiped at me like a meteorite. A meteorite that was bound to miss. "You can't hit an old man," I said.
He stomped off into the dark. I watched his broad shoulders struggle under his track jacket. He wasn't bright. A nice kid, but not smart. Heidi was smart.
"She's not going to show," I said. "She found out."
"You told her!" Akshay said.
"I didn't even know. How do I tell her? I didn't know. I mean, like right now, as of now, I don't know who it was… Elizabeth? Or the cousin, the, the girl with the freckles…"
"Neither of them," Akshay said. He added, "You are a fool."
"Ohhh," I said. "Neither of them. So it's somebody."
He looked at me and then swatted his hand down like he was angry with a big lever. "Your life is nothing," he said.
I admit this was a blow I didn't see coming.
"It's all right," I said of my life. "It's something."
"It's nothing," he said. "You sit here and write in your notebook and nothing gets done, and you make trouble for the girls."
"I do not," I said. But I knew I looked at their legs, and maybe they noticed.
Akshay, this new fearsome Akshay, began to sing. "Nowheeere Man," he pronounced. "Nowheeeeeere Man. The wo-o-o-o-orld is at your—"
Legs flashing, Heidi emerged from the dark. She swung her arm around Akshay's waist and pulled him in for a kiss. The two of them strolled into the nest of dark between the streetlights, and I think her chin was near his ear. The revelation of the Goleman book flyleaf had been forgiven.
True enough, because a thud followed: Heidi had tossed something behind her. At the streetlight's edge lay a dim little rectangle, and I spotted (I think) a blue border and red letters. It was Emotional Intelligence, I'm pretty sure. I've never gone to look.
So my 40s ended. I've never felt older, but perhaps I was taught a few things.
—Follow C.T. May on Twitter: @CTMay3
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text:
An Open, Honest Letter to England Fans …
Dev Bajwa
The life of a Three Lions supporter hasn't been the easiest since the famous World Cup triumph of 1966, but certain things need to be said following this summer's European Championship campaign.
Ever since a somewhat surprising World Cup semi-final outing in 2018, England fans have ranked themselves right up there with some of the most optimistic in the world. Finally believing that their 50+ year wait to bring home a major trophy was going to end in the grandest of manners possible. And we really did come close this time.
En route to enduring their first major tournament final since the heroes of '66, England fans nationwide had good reason to feel positive about their lads' prospects for going all the way. After topping the group with relative ease (and without losing a single game), what could have been a run-in through the toughest teams of Group F, turned into an historic showdown with ex-enemies Germany before aligning the stars for a streamlined journey to the end.
Setting to their Ukrainian opponents in earnest with a 4-0 win, an emotional, topsy-turvy triumph over Denmark solidified their place in the competition's parting piece. However, they were going to have to work hard to get anything out of an occasion like that against a team like Italy – statistically the best team of the tournament, and one which hasn't lost a single game in their previous 34 in all competitions. Yikes!
We've spoken previously about the refreshing philosophy of Roberto Mancini's Italy side, though I doubt sentiments like that did little to quell the general optimism held by England fans about this. Believing we would rise to the standard required to see off the Italians, all eyes were on Wembley Stadium on Sunday evening.
As it would come to pass, a penalty shoot-out (the Bain of the existence for players and England fans alike) would be the telling difference between the two sides as Italy lifted the trophy they'd largely deserved to for their consistency throughout the past few weeks. But where I'd hope to summarise by commending the reactions to this loss by the Three Lions faithful, my mood this morning is one of shame and embarrassment.
Failing to convert their crucial spot kicks, Marcus Rashford, Jadon Sancho and Bukayo Saka have been lambasted with a torrent of abuse from their own supporters. With many so-called 'England fans' choosing to centre on their race as a means of bringing them even lower than what they must already be feeling at this point.
Which brings me to the reason I'm writing this open letter to England fans, on behalf of all those associated with Ultra United. Our purpose has always been to deliver unbiased, wide-ranging content to educate the everyday supporter on the beauty of the game – which largely derives from the idiosyncrasies with which different areas of the world approach the same sport. And this theme of respect and integrity rings true through it all.
But when I scroll through social media this morning or reflect on how certain 'England fans' have responded to this defeat, I have been absolutely disgusted by the abhorrent actions of those who bring our game into disrepute. Worse still, actions which remain largely reaction-less due to the negligence of our major social networking platforms to condemn such behaviour.
And I just want to say this, in a direct address to those who have dared to single out our players in this manner: you have no business being a football supporter, and you don't ever deserve to associate yourself with our game again. These lads go through a level of pressure that neither you or I could ever truly appreciate. They wear their heart on their sleeve and make us proud with how they conduct themselves as professionals. They deserve better.
What do you make about some of the behaviour of England fans throughout the course of last night's final? Let us know in the comments and share our post if you agree with us.
Tags:Bukayo SakaEngland National TeamEuro 2020Gareth SouthgateJadon SanchoMarcus Rashford
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BumpyKanahele.com
Saturday, 9:30am
Iolani Palace at the Ahu
FEDERAL LOAN DATA DEMONSTRATES
NEED FOR HAWAIIAN BANK
Analysis of Hawai`i home loan refinancing data from 2005 recently released by the Federal Financial Institutions Examination Council (FFIEC) reveal that applications for mortgage loan refinancing submitted to Hawai`i's banks from native Hawaiians and other Pacific Islanders (NHOPI) continue to be denied at a significantly higher rate than applications submitted by Asians and Caucasians.
In fact, Asians in the lowest income bracket (less than 50% of median income) had lower denial rates than Hawaiians and other Pacific Islanders in the highest income bracket (120% of median income).
It is important to remember that those who are applying to refinance their homes are already homeowners who are currently paying their mortgages and are simply trying to refinance at a lower interest rate.
These findings are drawn from FFIEC data aggregated from all Hawai`i banks, and the discrepancy is across the board. The lending record of specific Hawai`i banks varies considerably. For example, First Hawaiian Bank denied refinancing applications from native Hawaiians and other Pacific Islanders at a rate 4.5 times greater than applications from Caucasians and 3 times the denial rate for Asians.
This data is particularly interesting given First Hawaiian's report of record profits this week and the announcement that one of its key officers in charge of community reinvestment is one of the three finalists for Trustee of the Kamehameha Schools.
Along with demonstrating the need for banks to be more responsive to Hawaiians' needs, this data also highlights the importance of establishing a bank specifically to serve the Hawaiian and pacific island community, using existing Hawaiian assets, which is owned and controlled by the Hawaiian people.
The banking needs of the Asian community in Hawai`i are being served well in part because they started banks years ago that are tailored to fit with their culture and needs.
With the Office of Hawaiian Affairs and other Hawaiian institutions, the Hawaiian community has far more collective financial assets than the Asian community did when they started their banks.
Through stock ownership by the Hawaiian people, the bank would be structured to give Hawaiians democratic control over their collective financial assets.
All that is lacking is the political will.
Like the banks that serve the Asian population, a Hawaiian bank would serve everyone else as well, but it would focus on reaching out to the Hawaiian people in a culturally responsive way and providing financial products and services tailored to fit with their needs. It could also serve other Pacific Islanders who have similar cultures and needs as well.
In general, a Hawaiian bank would reinvest in Hawai`i and incorporate Hawaiian values like ohana, malama aina, and laulima into its banking practices in a way that will benefit everyone in the islands.
The Nation of Hawai`i was instrumental in establishing Bank of America's commitment to supporting a Hawaiian bank, and also the federal regulations which require Native Hawaiian statistics to be generated for lending data which makes this analysis possible. But this data shows other banks also have as much difficultly lending to Native Hawaiians. Bank of America is still obligated under a longstanding commitment to support the establishment of a Hawaiian bank, and we would like other banks to support it as well, and not resist it.
E Komo mai,
Dennis "Bumpy" Kanahele
Nation of Hawai`i, Head of State
Malama First LLC, Managing Member
Aloha First, CEO
2006 Candidate at Large, for the Office of Hawaiian Affairs
"I have dedicated my life to addressing the long-standing issues and concerns of our people. I do this for, 'The Love of Country' and to secure the foundation of our Future."
-- Pu'uhonua D. K. Kanahele
(ceded)
CON-CON INFORMATION
NATION OF HAWAII
UNITED NATIONS DECOLONIZATION
1985 PDF
REQUEST FOR OHA
(updates)
LINGLE APPEALS RULING
SUPREME COURT DECISION
BACKGROUND / BIO
MALAMA FIRST HEALTH
HAWAIIAN ECONOMICS


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text:
Jajpurattus incertus är en spindelart som beskrevs av Prószynski 1992. Jajpurattus incertus ingår i släktet Jajpurattus och familjen hoppspindlar. Inga underarter finns listade i Catalogue of Life.

Källor

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incertus

redpajama_set_name: RedPajamaWikipedia


Peak fall. It's happening... now.
So you'd better find yourself some cider to sip.
Here's one: Shacksbury Lost and Found from Vermont. It's a limited seasonal release of only 240 cases that you can and should drink right now.
This stuff is made with "lost apples" from the Champlain Valley. See, a bunch of apple trees were planted over a century ago by homesteaders who were making their own cider the old super-New-England-y way. Said trees weren't maintained in orchards over the years and were eventually forgotten, so now they're all just hanging out in the wild. But fortunately for us, they were rediscovered by a couple of guys who like to forage around for old apple trees and then blend that fruit with New Hampshire heirloom apples, thereby making this cider.
Which is a 6.5% ABV unfiltered cider with a good dose of funk to it. It's extra dry with a tartness that's sour but not too sour.
In short, it's about as far as you can get from the Sprite-esque snake oil that passes for cider at most bars these days. And a lot closer to what Robert Frost would've sipped on his porch while contemplating the taking and not taking of roads.
Robert Frost just feels like a dry cider guy.


Citation needed, some online casinos claim higher payback percentages for slot machine games, and some publish payout percentage audits on their websites.Online casinos, also known as virtual casinos or, internet casinos, are online versions of traditional brick and mortar casinos.Some online casinos also allow gameplay through a html interface.Games are mainly represented in the browser plugins Macromedia Flash, Macromedia Shockwave, or Java and require browser support for these plugins.However, usually the player has to trust that the software has not been rigged to increase the house edge, as its inner workings are invisible to the user.Online casinos enable gamblers to play and wager on casino games through the, internet.Archived from the original.
7 Germany The German Online Casino Legislative 2012 (EU) many will can you bonus loot if lockout often describes as very complex e European Commission via the "EU Pilot" scheme recently questioned Germany about what steps it plans to take to eliminate the current sports-betting monopoly operated by state-owned Oddset and.
These games are a lot more expensive for websites to host than virtual games, as they involve a heavier investment in technology and staffing.Also, bandwidth is needed since all graphics, sounds and animations are loaded through the web via the plugin.See also Notes Interactive Gambling Act 1998 - Australia Cowie, Tom.Canadian Gaming Lawyer Magazine.Archived from the original on 7 November 2012.However, this exodus did not last long in most cases as the benefits outweighed the stumbling blocks, due to the UK being a major market for online gambling.Citation needed United States In the United States, the legality of online gambling is debated and can vary from state to state.


My Fabulous Class: 500 Follower FREEBIE and GIVEAWAY!
This has 36 different schedule cards and I am willing to modify it if you need anything else added. You get this free at my store.
I also have these adorable polka-dot cards free for FACEBOOK fans. You can find my Polka-Dot Classroom decor in my Store.
I have asked a few of my favorite bloggers to donate a product for my 500 follower giveaway.
Laura at Differentiation Station Creation has generously donated her Romp and Stomping Letters.
Sarah at Educlips has donated her Apple Clip Art Bundle.
Molly at Kinder Molly has donated her Chalk Classroom Library Labels.
Tara at Little Minds at Work has donated one of her best selling items Teacher, Teacher How Do I Spell...?
Tiffany at KTeacherTiff has donated her Interactive Math Notebook.
And I am donating my Beginning Sounds Mega Pack.
You can enter below by following all of these fabulous ladies on Teachers Pay Teachers. You can get a 5 point bonus by following me on Facebook and a 10 point bonus by sharing this Giveaway.


100 Thieves CEO Matthew 'Nadeshot' Haag and League of Legends Head Coach Neil 'pr0lly' Hammad sat down to reveal major changes they anticipate to make following a lackluster Spring Split.
For their sophomore year in the LCS, 100 Thieves put together a team deep with star talent but never managed to string the results they expected. Even with former World Champions and All-Star caliber players, the team placed dead last in a split they had topped out in the year before.
As the org looks to pick itself up, management gave some insights on what's been going wrong as well as the restructuring they plan to make from everything to the roster, coaches on stage, and even their team house.
Pr0lly is one of the most well-known coaches in League of Legends history.
Nadeshot is still confident in pr0lly to lead the squad on stage, but it looks like he's going to have some help in the future.
The two confirmed that they'll be bringing back a performance coach, as well as teased more integration from Academy coach Kelsey Moser.
100T has a team composed of stars who have all been in dire straights before. But upper management thinks that the team could benefit from a sports psychologist in case the star-studded lineup need a bit of direction when things start to fall apart.
Changes will be made. Although any move will be a surprise for fans since every starting position is currently filled with a former Champion in domestic or international stages, the team isn't expecting to field the same iteration again.
The 100 Thieves roster was expected to do great things during the 2019 Spring Split.
The Rocket Mortage house has been one of the centerpieces of this 100 Thieves team.
Pr0lly has been noticing that the 100 Thieves have been dealing with an "unknown phenomenon" in that their scrims have been great but the progress isn't translating to the LCS stage.
To combat that, they're working with their sponsor to restructure the team's practice facility in a way that will help players naturally apply what they learn in practice into pro games.
Whether it's bringing on specialists to help the team's morale and mentality during downward skids or equipping the roster with cohesive parts, the 100 Thieves org are going to make sweeping changes to pick themselves up from a disappointing 2019 campaign.


1. Why do you choose to do business in Mendon?
2. What are the benefits to doing business in Mendon?
3. In what ways could the town of Mendon improve your ability to do business in this town?
4. How do you, as a business owner, feel that the town of Mendon could draw more business to town?
5. If more infrastructure was offered by the town, would you accept a dual tax rate?
6. Is it desirable to have more/complimentary business in town?
7. What kinds of businesses would be undesirable for your own?
8. Do you need additional support from town? — If so, explain.
9. Would a marijuana facility have any impact on your business?
10. Would you like to see Mendon offer municipal lots for shared parking for businesses?
11. Are there services you might need that are not currently offered in town?
Our monthly business showcase will feature a Mendon business on the Mendon EDC's Facebook page, a spotlight on the town website, and a visit from the Mendon Economic Development Committee. Participating is a great way to get the word out about your business to the greater community. We hope to see you participate.
You may respond either by email to Tmerolli@mendonma.gov or by mail at 20 Main St, Mendon MA, 01756.


A new TV series based on the classic "Mighty Ducks" films is in the works.
It's early days for the project, says THR, but screenwriter Steven Brill (writer of the film trilogy) and producer Jordan Kerner are spearheading the project, suggesting the series may be connected to the films in some way.
Though ABC Signature Studios are producing the project, the series doesn't yet have a home. The trade says to expect a Disney affiliate to pick it up.
The original 1992 film, directed by Stephen Herek and starring Emilio Estevez, Lane Smith and a then-unknown Josh Jackson, was followed by two sequels and an animated TV series.


Welcome to the Lyft - Martinsburg Taxi Fare Finder. This page will calculate your cab fare using Lyft - Martinsburg taxi rates. To begin, enter your travel information in the fields below the map.
How much does Lyft cost in Martinsburg?
Minimum fare is $4.60.Cancellation fee is $7.00. Tolls are additional.


Purchase guaranteed parking for In the Heights at Broadway Sacramento in Sacramento on Saturday, August 24, 2019 at 7:30 PM. Parking passes are 100% guaranteed for entry at your selected parking location within the hours of operation shown at each location. Enjoy In the Heights parking at Broadway Sacramento and park happy with SacPark today!


Show off your silhouette in the Naples Dress. Crafted from our signature polyamide which is known for its sleek and fitted silhouettes, this mini length style features princess seams at the bust with its plunging vee neckline. The Naples Dress sculpts your silhouette with its self-tie at back and keyhole feature.


text:
PUBG Welcome gift pack leaked in India, PUBG Mobile to launch in...
According to the sources, PUBG Mobile India was coming soon in 1 or 2 months but it has to follow many government guidelines.
From past few months Government of India had banned many Chinese apps and PUBG mobile India was one of them but theirs a piece of good news for all the PUBG lovers that their favourite game is going to return in India.
The gaming company recently has also released a teaser of its new version on social media sites. After the release of the teaser, the Welcome gift of PUBG mobile India was leaked. It saw Anarkali with headgear.
It is said that Data Miners and YouTubers have seen the welcome gift. All the players will get a welcome gift when they download the game and play. In the beta version Anarkali headgear, sets and the classic coupon were seen. All the new features are currently in its testing phase. There is a lot of enthusiasm seen in the players. But until the final nod is not given from the Government of India the game will not launch, as it has to follow many strict data guidelines.
The PUBG corporation says that it has approached the Ministry of Electronics and Information Technology for its launch but the Ministry has not yet responded. In this situation, a confirmation can't be given when PUBG mobile India will launch again.


text:
Disability Royal Commission to bring welcome spotlight on sector
HSU National today welcomed the bi-partisan passing of a motion supporting the establishment of a royal commission to inquire into violence, abuse and neglect of people with a disability.
Branches of HSU National proudly cover workers in the disability sector in a number of states.
"HSU National congratulates everyone who played role in achieving this historic milestone. Disability voices will no longer be silent," said HSU National Secretary Lloyd Williams.
"However, it is critical that the Government gets the Royal Commission Terms of Reference right and consult genuinely with stakeholders, including participants, carers, families, experts, as well as workers and their representatives," said Mr Williams.
"Workers in the disability sector are committed, passionate and skilled. Their experiences and knowledge will be critical to ensuring that this Royal Commission is meaningful and leads to real reform."
"All workers in the disability sector want to create a fair, equitable and quality NDIS and disability sector."
The 2015 Senate inquiry which found widespread rates of violence and abuse against people with a disability, and which strongly recommended holding a royal commission, was a wake-up call to Government and this overdue action should be celebrated by all sector stakeholders.
Notes: HSU branches are campaigning to put the heart back in the NDIS, read more about the campaign at: https://hsu.net.au/action/support-disability-support-workers/
Download PDF Document
Mick Bellairs
National Campaigns and Projects Officer
mickb@hsu.net.au, 0419 777 380
16 Days of Activism 'Permaflexi' could erode conditions for the lowest paid
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The forecast Federal Budget surplus, built on the back of a $1.6b NDIS 'underspend', showed the country that this Government's […]
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This news blast is suggesting that AP holders are being asked for photo id. Anyone there and experiencing this? Much speculation will follow, I am sure, as to what this is about, but I truly hope that this is a short-term issue due to a system failure and not a long-term plan!
This trip I was asked to show a drivers license or picture ID before entering Boardwalk. Last year I think I just had my magic band swiped. Is this a new procedure .
I also over near a family complaining bitterly at beaches and cream that they had to park at the studios and walk over . Is that also the new procedure?
I understand why. There is not that much guest parking at Boardwalk and apparently they are loosing 100 more spaces . I can see why they are cutting back in letting non resort guest to park there.
I wonder if MK resorts are also asking dinner guests to park at tct . Will the Restaurent validate the parking?
if you are greeted by a gate guard they normally ask. Even in Uber or Lyft.
If you enter through automatic gate, you can tap and go but there are instances they Guard that 1 as well. The auto gate you need to be staying at the resort to access.
And there's always valet parking for those visiting.
No validation I know of since DVC perk went away. Not sure if TIW still offers parking. I believe the SD restaurants will validate if you dine with them.
Yup, do believe I've had that before as well.
They've asked to see ID's for years when you drive up to the gate. Once you had your Key to the World card or now MB then you can use the electronic gate.
However this action has always just left me shaking my head. Yes, I'm proving I have a drivers license or photo id that has my picture on it.
Regarding parking at BWV or BC - it can be hit or miss and also has been that way for a long time. If you have a Beaches and Cream reservation though then parking should be allowed at BC. I don't believe it would be allowed at BWV based on that though.
I've shown ID as well for quite a while the first time we checked in as well.
As for parking, it always depends on how busy they are. I always keep in mind if not staying there, that if I want to drive, I might have to pay for valet.
This is a PRIME example of "security theatre"
What exactly does this prove besides you have a driver's license???