The Information Criterion

被引：0

作者：

Ghahramani, M. ^{[1
]}

机构：

[1] Payam Noor Univ, Dept Math Stat, Tehran, Iran

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2014年 / 13卷 / 02期

关键词：

Consistency; AIC; information criterion; Kullbake-Leibler risk; model selection;

D O I：

暂无

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：

The Akaike information criterion, AIC, is widely used for model selection. Using the AIC as the estimator of asymptotic unbias for the second term Kullbake-Leibler risk considers the divergence between the true model and offered models. However, it is an inconsistent estimator. A proposed approach the problem is the use of A'IC, a consistently offered information criterion. Model selection of classic and linear models are considered by a Monte Carlo simulation.

引用

页码：444 / 454

页数：11

共 10 条

[1]

Akaike, 1973, 2 INT S INF THEOR, P267, DOI DOI 10.1007/978-1-4612-1694-0_15

[2] Akaike's information criterion and recent developments in information complexity [J].