Variable selection in infinite-dimensional problems

被引：51

作者：

Aneiros, German ^{[1
]}

Vieu, Philippe ^{[2
]}

机构：

[1] Univ A Coruna, Dept Matemat, La Coruna, Spain

[2] Univ Toulouse 3, Inst Math, F-31062 Toulouse, France

来源：

STATISTICS & PROBABILITY LETTERS | 2014年 / 94卷

关键词：

Functional data analysis; Variable selection; High-dimensional problem; Partitioning variable selection procedure; NONCONCAVE PENALIZED LIKELIHOOD; DANTZIG SELECTOR; MODEL SELECTION; REGRESSION; LASSO;

D O I：

10.1016/j.spl.2014.06.025

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper is on regression models when the explanatory variable is a function. The question is to look for which among the p(n) discretized values of the function must be incorporated in the model. The aim of the paper is to show how the continuous structure of the data allows to develop new specific variable selection procedures, which improve the rates of convergence of the estimated parameters and need much less restrictive assumptions on p(n). (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：12 / 20

页数：9

共 17 条

[1]

[Anonymous], 2006, Journal of the Royal Statistical Society, Series B

[2] SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR [J].