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

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 transfor...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 1999-06, Vol.63 (3), p.263-281
Main Authors: Campbell, Nancy L., Young, Linda J., Capuano, George A.
Format: Article
Language:English
Subjects:
Count data
cross-classification
Distribution theory
Exact sciences and technology
generalized linear model
Linear inference, regression
Mathematics
Multivariate analysis
negative binomial distribution
overdispersion
Probability and statistics
Sciences and techniques of general use
Statistics
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Summary: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.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949659908811956