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Activity Number: 444 - Highlights from the Journal STAT
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: SSC (Statistical Society of Canada)
Abstract #312226
Title: Penalized Euclidean Distance Regression
Author(s): Daniel Vasiliu* and Ian Dryden and Tanujit Dey
Companies: College of William & Mary and University of Nottingham and Harvard Medical School and Brigham and Women's Hospital

A method is introduced for variable selection and prediction in linear regression problems where the number of predictors can be much larger than the number of observations. The methodology involves minimizing a penalized Euclidean distance, where the penalty is the geometric mean of the ?1 and ?2 norms of regression coefficients. This particular formulation exhibits a grouping effect, which is useful for model selection in high?dimensional problems. Also, an important result is a model consistency theorem, which does not require an estimate of the noise standard deviation. An algorithm for estimation is described, which involves thresholding to obtain a sparse solution. Practical performances of variable selection and prediction are evaluated through simulation studies and the analysis of real datasets.

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

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