<p dir="auto">In the current toolchain <a href="https://scipy.github.io/devdocs/dev/toolchain.html#numpy" rel="nofollow">roadmap</a> page, I find the information regarding Python versions confusing as the last versions of SciPy are not listed and also there is no information about the max Python version for a given version of SciPy.</p>
<p dir="auto">I propose to change the table with the following columns:</p>
<table role="table">
<thead>
<tr>
<th>SciPy version</th>
<th>Python versions</th>
<th>NumPy versions</th>
</tr>
</thead>
<tbody>
<tr>
<td>1.7</td>
<td>&gt;3.7, &lt;=3.10</td>
<td>&gt;=1.16.5, &gt;= 1.20.x</td>
</tr>
</tbody>
</table>
<p dir="auto">Right now, I am not sure for most items about the max Python version. This should also answer: should we drop Python 3.7 for 1.8? Same goes for NumPy 1.16.5.</p>
<p dir="auto"><strong>Is your feature request related to a problem? Please describe.</strong><br>
The problem is to have an estimator of location (i.e. the mean) in 1 dimension that is not disrupted by outliers data and that is efficient in heavy-tailed setting. This is partly solved by the median but there are estimators that are much more efficient than the median when, for example, we want to estimate the mean of a corrupted Gaussian distribution.</p>
<p dir="auto"><strong>Describe the solution you'd like</strong></p>
<p dir="auto">I'd like to implement Huber's estimator of location. This is an M-estimator, it is already <a href="https://www.rdocumentation.org/packages/MASS/versions/7.3-53.1/topics/huber" rel="nofollow">included in R library "MASS"</a> for instance. This is similar but different from <a href="https://www.statsmodels.org/stable/generated/statsmodels.robust.scale.Huber.html" rel="nofollow">statsmodel implementation</a> in part because statsmodel implementation is more complex than what I propose and it suffers a low breakdown point (number of outliers tolerated by the estimator).</p>
<p dir="auto">Huber estimator is very famous in robust statistics and has been used for quite some time now. In particular, one can look at the seminal article [1] but also at the books [2], [3] and any google scholar search will tell you that it is widely used.</p>
<p dir="auto">There are several ways to compute this estimator but one of the simplest is the iterative reweighting algorithm found in [2].</p>
<p dir="auto"><strong>Describe alternatives you've considered</strong><br>
None</p>
<p dir="auto"><strong>Additional context (e.g. screenshots)</strong><br>
None</p>
<p dir="auto">[1] Robust Estimation of a Location Parameter,  Peter J. Huber, Ann. Math. Statist (1964)<br>
[2] Robust Statistics, Peter J. Huber and Elvezio M. Ronchetti , Wiley Series in Probability and Statistics (2009)<br>
[3] Robust Statistics: The Approach Based on Influence Functions, Frank R. Hampel, Elvezio M. Ronchetti, Peter J. Rousseeuw, Werner A. Stahel, Wiley (2011)</p>