Analyzing over-dispersed count data in two-way cross-classification problems using generalized linear models

被引：5
|
作者：
Campbell, NL [1 ]
Young, LJ
Capuano, GA
机构：
[1] John Carroll Univ, Dept Math & Comp Sci, University Hts, OH 44118 USA
[2] Univ Nebraska, Dept Biometry, Lincoln, NE 68583 USA
来源：
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 1999年 / 63卷 / 03期
关键词：
count data; generalized linear model; negative binomial distribution; over-dispersion; cross-classification;
D O I：
10.1080/00949659908811956
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Several methods of testing factor effects in a two-way cross-classification analysis are compared in a Monte Carlo simulation study, using over-dispersed count data generated from the family of negative binomial distributions. Tests compared are based on general linear models, using raw and transformed data, and on generalized linear models specifying either the Poisson or negative binomial distribution. The general linear model is recommended, especially in the case of small means and small numbers of replications.
引用
收藏
页码：263 / 281
页数：19
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