Model selection in linear regression

被引：12

作者：

Kundu, D ^{[1
]}

Murali, G ^{[1
]}

机构：

[1] INDIAN INST TECHNOL,DEPT MATH,KANPUR 208016,UTTAR PRADESH,INDIA

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 1996年 / 22卷 / 05期

关键词：

model selection; AIC; BIG; EDC; consistent estimates; penalized likelihood;

D O I：

10.1016/0167-9473(96)00008-4

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We consider the multiple regression model Y = X(0) beta + epsilon, where Y and epsilon are n-vector random variables, X(0) is an n x m design matrix and beta is an in-vector of unknown regression parameters. It is well known that different information theoretic criteria with proper choice of penalty function can be used to choose the correct model. In this paper we have done an extensive simulation study to choose the proper penalty function, by using different models and using different error random variables.

引用

页码：461 / 469

页数：9

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