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Am. Stat. Assoc.</a> <a href="/?q=in%3A451409" title="Articles in this Issue">115, No. 529, 254-265 (2020)</a>. </div> <div class="abstract">Summary: Big data can easily be contaminated by outliers or contain variables with heavy-tailed distributions, which makes many conventional methods inadequate. To address this challenge, we propose the adaptive Huber regression for robust estimation and inference. The key observation is that the robustification parameter should adapt to the sample size, dimension and moments for optimal tradeoff between bias and robustness. Our theoretical framework deals with heavy-tailed distributions with bounded \((1+\delta)\)th moment for any \(\delta>0\). We establish a sharp phase transition for robust estimation of regression parameters in both low and high dimensions: when \(\delta\geq1\), the estimator admits a sub-Gaussian-type deviation bound without sub-Gaussian assumptions on the data, while only a slower rate is available in the regime \(0<\delta<1\) and the transition is smooth and optimal. In addition, we extend the methodology to allow both heavy-tailed predictors and observation noise. Simulation studies lend further support to the theory. In a genetic study of cancer cell lines that exhibit heavy-tailedness, the proposed methods are shown to be more robust and predictive.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A7227129">Cited in <strong>78</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A62J02" title="MSC2020">62J02</a> </td> <td class="space"> General nonlinear regression </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62F35" title="MSC2020">62F35</a> </td> <td class="space"> Robustness and adaptive procedures (parametric inference) </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A62G32" title="MSC2020">62G32</a> </td> <td class="space"> Statistics of extreme values; tail inference </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Aadaptive+Huber+regression">adaptive Huber regression</a>; <a href="/?q=ut%3Abias+and+robustness+tradeoff">bias and robustness tradeoff</a>; <a href="/?q=ut%3Afinite-sample+inference">finite-sample inference</a>; <a href="/?q=ut%3Aheavy-tailed+data">heavy-tailed data</a>; <a href="/?q=ut%3Anonasymptotic+optimality">nonasymptotic optimality</a>; <a href="/?q=ut%3Aphase+transition">phase transition</a></div> <!-- Modal used to show zbmath metadata in different output formats--> <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;"> <!-- alert for connection errors etc --> </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function"> <!-- Button trigger metadata modal --> <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 1437.62250" data-ciurl="/ci/07227129" data-biburl="/bibtex/07227129.bib" data-amsurl="/amsrefs/07227129.bib" data-xmlurl="/xml/07227129.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/07227129.pdf" title="Zbl 1437.62250 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1080/01621459.2018.1543124" aria-label="DOI for “Adaptive Huber regression”" title="10.1080/01621459.2018.1543124">DOI</a> <a class="btn btn-default btn-xs" type="button" href="https://arxiv.org/abs/1706.06991"title="Note: arXiv document may differ from published version">arXiv</a> <a class="btn btn-default btn-xs" type="button" href="https://escholarship.org/uc/item/4sn8g6n9" title="Full Text Link">Link</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Alquier, P.; Cottet, V.; Lecué, G., “Estimation Bounds and Sharp Oracle Inequalities of Regularized Procedures With Lipschitz Loss Functions,”, arXiv no. 1702.01402 (2017)</td> </tr><tr> <td>[2]</td> <td class="space">Belloni, A.; Chernozhukov, V., Penalized Quantile Regression in High-Dimensional Sparse Models,”, The Annals of Statistics, 39, 82-130 (2011) · <a href="/1209.62064" class="nowrap">Zbl 1209.62064</a> · <a href="https://doi.org/10.1214/10-AOS827" class="nowrap">doi:10.1214/10-AOS827</a></td> </tr><tr> <td>[3]</td> <td class="space">Bellec, P. 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