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

＜journal article＞
Variable and boundary selection for functional data via multiclass logistic regression modeling

Creator	Author PID K004601 Creator Name Matsui, Hidetoshi 松井, 秀俊マツイ, ヒデトシ Affiliation Affiliation Name Faculty of Mathematics, Kyushu University 九州大学大学院数理学研究院
Language	English
Publisher	Elsevier
Date	2014-10
Source Title	Computational Statistics & Data Analysis
Vol	78
First Page	176
Last Page	185
Publication Type	Accepted Manuscript
Access Rights	open access
Related DOI	isIdenticalTo https://doi.org/10.1016/j.csda.2014.04.015
Related DOI	MI Preprint Series ; 2013-7
Related DOI	http://www.math.kyushu-u.ac.jp/
Related URI	http://www.math.kyushu-u.ac.jp/
Related URI
Related HDL
Relation	MI Preprint Series ; 2013-7
Relation	http://www.math.kyushu-u.ac.jp/
Abstract	L1 penalties such as the lasso provide solutions with some coefficients to be exactly zeros, which lead to variable selection in regression settings. They also can select variables which affect the cl...assification by being applied to the logistic regression model. We focus on the form of L1 penalties in logistic regression models for functional data, especially in the case for classifying the functions into three or more groups. We provide penalties that appropriately select variables in the functional multinomial regression modeling. Simulation and real data analysis show that we should select the form of the penalty in accordance with the purpose of the analysis.show more

File	FileType	Size	Views	Description
MI2013-7	pdf	177 KB	450

Record ID	26121
Peer-Reviewed	Refereed
Subject Terms	Functional Data Analysis
	Lasso
	Logistic regression model
	Model selection
	Regularization
ISSN	0167-9473
DOI	10.1016/j.csda.2014.04.015
NCID	AA10002391
Notes	The Global COE Program Math-for-Industry Education & Research Hub
Notes	グローバルCOEプログラム「マス･フォア･インダストリ教育研究拠点」
Created Date	2013.04.11
Modified Date	2024.01.10