Variable selection in regression models including functional data predictors

被引：0

作者：

Liu K. ^{[1
]}

Wang S. ^{[2
]}

机构：

[1] Big Data Center of Student Affairs Department, Beihang University, Beijing

[2] School of Statistics and Mathematics, Central University of Finance and Economics, Beijing

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2019年 / 45卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Functional data; Functional principal component; Parameter estimation; Quantile; Variable selection;

D O I：

10.13700/j.bh.1001-5965.2019.0157

中图分类号：

O212 [数理统计];

学科分类号：

摘要：

The variable selection and parameter estimation problem is researched in the framework of mixed-type regression model with both functional and multivariate predictors, which broadens the scope of functional data analysis and the application fields of variable selection methodology. First the functional predictors are projected into spaces spanned by functional principal component basis functions. Then variable selection and parameter estimation are implemented simultaneously for the multivariate predictors and derived projection predictors in the form of grouping, where the tuning parameter of the penalized term is adaptively selected and the loss function is based on absolute median loss function. As to the optimization procedure, by introducing slack variables, it is transformed into a linear programming problem with several constraint conditions, which simplifies the computation. The simulation results illustrate that the proposed method performs quite well in variable selection and parameter estimation in the mixed-type regression model. © 2019, Editorial Board of JBUAA. All right reserved.

引用

页码：1990 / 1994

页数：4

共 17 条

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