Replication Data for: BARP: Improving Mister P with Bayesian Additive Regression Trees
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units.  However, innovations in non-parametric regularization methods can further improve the researcher's ability to extrapolate opinion data to a geographic unit of interest. I test an ensemble of regularization algorithms and find that there is room for substantial improvement on the multilevel model via more sophisicated methods of regularization. I propose a modified version of MRP that replaces the multilevel model with a non-parametric approach called Bayesian Additive Regression Trees (BART or, when combined with post-stratification, BARP). I compare both methods across a number of data contexts, demonstrating the benefits of applying more powerful regulariza' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["BARP"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units.  However, innovations in non-parametric regularization methods can further improve the researcher's ability to extrapolate opinion data to a geographic unit of interest. I test an ensemble of regularization algorithms and find that there is room for substantial improvement on the multilevel model via more sophisicated methods of regularization. I propose a modified version of MRP that replaces the multilevel model with a non-parametric approach called Bayesian Additive Regression Trees (BART or, when combined with post-stratification, BARP). I compare both methods across a number of data contexts, demonstrating the benefits of applying more powerful regulariza' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units.  However, innovations in non-parametric regularization methods can further improve the researcher's ability to extrapolate opinion data to a geographic unit of interest. I test an ensemble of regularization algorithms and find that there is room for substantial improvement on the multilevel model via more sophisicated methods of regularization. I propose a modified version of MRP that replaces the multilevel model with a non-parametric approach called Bayesian Additive Regression Trees (BART or, when combined with post-stratification, BARP). I compare both methods across a number of data contexts, demonstrating the benefits of applying more powerful regulariza
BARP