Sparse group lasso for multiclass functional logistic regression models. Issue 6 (3rd July 2019)

• Record Type: Journal Article
• Authors: Matsui, Hidetoshi
• Is Part Of: Communications in statistics. Volume 48:Issue 6(2019)
• Page Start: 1784
• Page End: 1797
• Publication Date: 2019-07-03
• Subjects: Functional data analysis -- Lasso -- Model selection; 62-07 -- 62H30 -- 62J07; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2018.1423693
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.