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Activity Number: 241 - SLDS CPapers New
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #329358 Presentation
Title: A Cluster Elastic Net for Multivariate Regression
Author(s): Ben Sherwood* and Bradley S Price
Companies: University of Kansas and West Virginia University
Keywords: Penalized Regression; Multiple Responses; High Dimensional

We propose a method for estimating coefficients in multivariate regression when there is a clustering structure to the response variables. The proposed method includes a fusion penalty, to shrink the difference in fitted values from responses in the same cluster, and an L1 penalty for simultaneous variable selection and estimation. The method can be used when the grouping structure of the response variables is known or unknown. When the clustering structure is unknown the method will simultaneously estimate the clusters of the response and the regression coefficients. Theoretical results are presented for the penalized least squares case, including asymptotic results allowing for p larger than n. We extend our method to the setting where the responses are binomial variables. We propose a coordinate descent algorithm for both the normal and binomial likelihood, which can easily be extended to other generalized linear model (GLM) settings. Simulations and data examples are presented to show the merits of the proposed method.

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

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