Variable selection for 1D regression models

被引：11

作者：

Olive, DJ ^{[1
]}

Hawkins, DM

机构：

[1] So Illinois Univ, Dept Math, Carbondale, IL 62901 USA

[2] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA

来源：

TECHNOMETRICS | 2005年 / 47卷 / 01期

基金：

美国国家科学基金会;

关键词：

C-p; Cook's distance; generalized linear model; outlier; regression graphics; single index model;

D O I：

10.1198/004017004000000590

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：

Variable selection. the search for j relevant predictor variables from a group of p candidates is a standard problem in regression analysis. The class of ID regression models is a broad class that includes generalized linear models. We show that existing variable selection algorithms, originally meant for multiple linear regression and based on ordinary least squares and Mallows's C-P, can also be used for ID models. Graphical aids for variable selection are also provided.

引用

页码：43 / 50

页数：8

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