Regression diagnostic under model misspecification

被引：0
|
作者：
Chien, Li-Chu
Tsou, Tsung-Shan [1 ]
机构：
[1] Natl Cent Univ, Inst Stat, Inst Syst Biol & Bioinformat, Jhongli, Taiwan
[2] Natl Hlth Res Inst, Div Biostat & Bioinformat, Taipei, Taiwan
来源：
JOURNAL OF APPLIED STATISTICS | 2007年 / 34卷 / 05期
关键词：
influential diagnostic; robust likelihood; robust normal regression; DFBETAS; DFFITS; cook's distance;
D O I：
10.1080/02664760701235014
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 propose two novel diagnostic measures for the detection of influential observations for regression parameters in linear regression. Traditional diagnostic statistics focus on the effect of deletion of data points either on parameter estimates, or on predicted values. A data point is regarded as influential by the new methods if its inclusion determines a significantly different likelihood function for the parameter of interest. The concerned likelihood function is asymptotically valid for practically all underlying distributions whose second moments exist.
引用
收藏
页码：563 / 575
页数：13
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