Adaptive Huber Regression.

Adaptive Huber regression bias and robustness tradeoff finite-sample inference heavy-tailed data nonasymptotic optimality phase transition

Journal

Journal of the American Statistical Association
ISSN: 0162-1459
Titre abrégé: J Am Stat Assoc
Pays: United States
ID NLM: 01510020R

Informations de publication

Date de publication:
2020
Historique:
entrez: 3 11 2020
pubmed: 4 11 2020
medline: 4 11 2020
Statut: ppublish

Résumé

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 +

Identifiants

pubmed: 33139964
doi: 10.1080/01621459.2018.1543124
pmc: PMC7603940
mid: NIHMS1639544
doi:

Types de publication

Journal Article

Langues

eng

Pagination

254-265

Subventions

Organisme : NIGMS NIH HHS
ID : R01 GM072611
Pays : United States

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Auteurs

Qiang Sun (Q)

Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, Canada.

Wen-Xin Zhou (WX)

Department of Mathematics, University of California, San Diego, La Jolla, CA 92093.

Jianqing Fan (J)

Honorary Professor, School of Data Science, Fudan University, Shanghai, China and Frederick L. Moore '18 Professor of Finance, Department of Operations Research and Financial Engineering, Princeton University, NJ 08544.

Classifications MeSH