The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Generalized Linear Models with Random Effects; a Gibbs Sampling Approach
Abstract
Generalized linear models have unified the approach to regression for a wide variety of discrete, continuous, and censored response variables that can be assumed to be independent across experimental units. In applications such as longitudinal studies, genetic studies of families, and survey sampling, observations may be obtained in clusters. Responses from the same cluster cannot be assumed to be independent. With linear models, correlation has been effectively modeled by assuming there are cluster-specific random effects that derive from an underlying mixing distribution. Extensions of generalized linear models to include random effects has, thus far, been hampered by the need for numerical integration to evaluate likelihoods. In this article, we cast the generalized linear random effects model in a Bayesian framework and use a Monte Carlo method, the Gibbs sampler, to overcome the current computational limitations. The resulting algorithm is flexible to easily accommodate changes in the number of random effects and in their assumed distribution when warranted. The methodology is illustrated through a simulation study and an analysis of infectious disease data.
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Cites
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Cited by
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Metadata
- Authors
- Zeger, S. L., & Karim, M. R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1991-03
- Volume
- 86
- Issue
- 413
- Pages
- 79-86
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1997318672
- Cited by
- 987
- References
- 3
- FWCI
- 37.85
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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