CASE-DELETION DIAGNOSTICS FOR MIXED MODELS

被引：138

作者：

CHRISTENSEN, R

PEARSON, LM

JOHNSON, W

机构：

[1] MANNESMANN AG, DEPT MATH ASTRON & STAT, W-4100 DUISBURG 25, GERMANY

[2] UNIV CALIF DAVIS, DEPT STAT, DAVIS, CA 95616 USA

来源：

TECHNOMETRICS | 1992年 / 34卷 / 01期

关键词：

COOK DISTANCE; INFLUENCE; KRIGING; LEVERAGE; RANDOM EFFECTS; VARIANCE COMPONENTS;

D O I：

10.2307/1269550

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：

Mixed linear models arise in many areas of application. Standard estimation methods for mixed models are sensitive to bizarre observations. Such influential observations can completely distort an analysis and lead to inappropriate actions and conclusions. We develop case-deletion diagnostics for detecting influential observations in mixed linear models. Diagnostics for both fixed effects and variance components are proposed. Computational formulas are given that make the procedures feasible. The methods are illustrated using examples.

引用

页码：38 / 45

页数：8

共 20 条

[1] DIAGNOSTICS FOR MIXED-MODEL ANALYSIS OF VARIANCE [J].