Multiple linear regression model under nonnormality

被引：56
|
作者：
Islam, MQ [1 ]
Tiku, ML
机构：
[1] Cankaya Univ, Dept Econ, TR-06530 Ankara, Turkey
[2] Middle E Tech Univ, Dept Stat, TR-06531 Ankara, Turkey
[3] McMaster Univ, Hamilton, ON L8S 4L8, Canada
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2004年 / 33卷 / 10期
关键词：
multiple linear regression; modified likelihood; robustness; outliers; M estimators; least squares; nonnormality; hypothesis testing;
D O I：
10.1081/STA-200031519
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 multiple linear regression models under nonnormality. We derive modified maximum likelihood estimators (MMLEs) of the parameters and show that they are efficient and robust. We show that the least squares esimators are considerably less efficient. We compare the efficiencies of the MMLEs and the M estimators for symmetric distributions and show that, for plausible alternatives to an assumed distribution, the former are more efficient. We provide real-life examples.
引用
收藏
页码：2443 / 2467
页数：25
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