Penalized Maximum Likelihood Principle for Choosing Ridge Parameter

被引：7

作者：

Tran, Minh Ngoc ^{[1
,2
]}

机构：

[1] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117546, Singapore

[2] Vietnam Natl Univ, Singapore, Singapore

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2009年 / 38卷 / 08期

关键词：

Data-dependent penalty; Loss rank principle; Model selection; Penalized ML; Ridge parameter; Ridge regression; REGRESSION;

D O I：

10.1080/03610910903061014

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：

We consider the problem of choosing the ridge parameter. Two penalized maximum likelihood (PML) criteria based on a distribution-free and a data-dependent penalty function are proposed. These PML criteria can be considered as "continuous" versions of AIC. A systematic simulation is conducted to compare the suggested criteria to several existing methods. The simulation results strongly support the use of our method. The method is also applied to two real data sets.

引用

页码：1610 / 1624

页数：15

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