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Robust Multiple Regression

by 1,*,† and 1,2,†
Department of Statistics, Rice University, MS-138, 6100 Main Street, Houston, TX 77005, USA
Apple Corporation, Cupertino, CA 95014, USA
Author to whom correspondence should be addressed.
These authors contributed equally to the case studies, with the first author on earlier sections.
Entropy 2021, 23(1), 88;
Received: 16 December 2020 / Revised: 4 January 2021 / Accepted: 5 January 2021 / Published: 9 January 2021
As modern data analysis pushes the boundaries of classical statistics, it is timely to reexamine alternate approaches to dealing with outliers in multiple regression. As sample sizes and the number of predictors increase, interactive methodology becomes less effective. Likewise, with limited understanding of the underlying contamination process, diagnostics are likely to fail as well. In this article, we advocate for a non-likelihood procedure that attempts to quantify the fraction of bad data as a part of the estimation step. These ideas also allow for the selection of important predictors under some assumptions. As there are many robust algorithms available, running several and looking for interesting differences is a sensible strategy for understanding the nature of the outliers. View Full-Text
Keywords: minimum distance estimation; maximum likelihood estimation; influence functions minimum distance estimation; maximum likelihood estimation; influence functions
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Scott, D.W.; Wang, Z. Robust Multiple Regression. Entropy 2021, 23, 88.

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Scott DW, Wang Z. Robust Multiple Regression. Entropy. 2021; 23(1):88.

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Scott, David W., and Zhipeng Wang. 2021. "Robust Multiple Regression" Entropy 23, no. 1: 88.

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