MODEL SELECTION FOR LEAST ABSOLUTE DEVIATIONS REGRESSION IN SMALL SAMPLES

被引：45

作者：

HURVICH, CM ^{[1
]}

TSAI, CL ^{[1
]}

机构：

[1] UNIV CALIF DAVIS,DIV STAT,DAVIS,CA 95616

来源：

STATISTICS & PROBABILITY LETTERS | 1990年 / 9卷 / 03期

关键词：

AIC; AICR; cAIC; L1; regression;

D O I：

10.1016/0167-7152(90)90065-F

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 develop a small sample criterion (L1cAIC) for the selection of least absolute deviations regression models. In contrast to AIC (Akaike, 1973), L1cAIC provides an exactly unbiased estimator for the expected Kullback-Leibler information, assuming that the errors have a double exponential distribution and the model is not underfitted. In a Monte Carlo study, L1cAIC is found to perform much better than AIC and AICR (Ronchetti, 1985). A small sample criterion developed for normal least squares regression (cAIC, Hurvich and Tsai, 1988) is found to perform as well as L1cAIC. Further, cAIC is less computationally intensive than L1cAIC. © 1990.

引用

页码：259 / 265

页数：7

共 9 条

[1]

Akaike H., 1992, 2 INT S INF THEOR, P267, DOI DOI 10.1007/978-1-4612-1694-0_15

[2]

[Anonymous], 1983, LEAST ABSOLUTE DEVIA

[3] A PROPERTY OF MAXIMUM LIKELIHOOD ESTIMATORS OF LOCATION AND SCALE PARAMETERS [J].