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Activity Number: 245 - SLDS CSpeed 4
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319044
Title: Robust Multiple Regression
Author(s): Zhipeng Wang* and David Warren Scott
Companies: Rice University and Rice University
Keywords: minimum distance criteria; maximum likelihood estimation; influence functions; robustness; outliers

In the big data era, data contamination and outliers are widely existed. They have negative influence on the performance of statistical models. 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 work, we have proposed 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.

Authors who are presenting talks have a * after their name.

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