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Activity Number: 693
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #319710 View Presentation
Title: Regularized Ordinal Logistic Regression via Coordinate Descent
Author(s): Michael Wurm* and Bret Hanlon
Companies: University of Wisconsin - Madison and University of Wisconsin - Madison
Keywords: ordinal regression ; regularization ; variable selection
Abstract:

Ordinal regression models arise in contexts where the response variable belongs to one of several ordered categories (such as 1="poor", 2="fair", 3="good", 4="excellent"). Ordinal logistic regression (also known as proportional odds or ordered logit regression) is widely used in applications where the use of regularization and variable selection could be beneficial. However, ordinal logistic regression is not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit ordinal logistic regression with elastic net penalty. We also introduce the R package ordinalNet, which implements the algorithm.


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

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