Variable and boundary selection for functional data via multiclass logistic regression modeling

Hidetoshi Matsui

Computational Statistics & Data Analysis, 2014, vol. 78, issue C, 176-185

Abstract: Penalties with an ℓ1 norm provide solutions in which some coefficients are exactly zero and can be used for selecting variables in regression settings. When applied to the logistic regression model, they also can be used to select variables which affect classification. We focus on the form of ℓ1 penalties in logistic regression models for functional data, in particular, their use in classifying functions into three or more groups while simultaneously selecting variables or classification boundaries. We provide penalties that appropriately select the variables in functional multiclass logistic regression models. Analysis of simulation and real data show that the form of the penalty should be selected in accordance with the purpose of the analysis.

Keywords: Functional data analysis; Lasso; Logistic regression model; Model selection; Regularization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:78:y:2014:i:c:p:176-185

DOI: 10.1016/j.csda.2014.04.015

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