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Multinomial logit random effects models
Abstract
This article presents a general approach for logit random effects modelling of clustered ordinal and nominal responses. We review multinomial logit random effects models in a unified form as multivariate generalized linear mixed models. Maximum likelihood estimation utilizes adaptive Gauss-Hermite quadrature within a quasi-Newton maximization algorithm. For cases in which this is computationally infeasible, we generalize a Monte Carlo EM algorithm. We also generalize a pseudo-likelihood approach that is simpler but provides poorer approximations for the likelihood. Besides the usual normality structure for random effects, we also present a semi-parametric approach treating the random effects in a non-parametric manner. An example comparing reviews of movie critics uses adjacent-categories logit models and a related baseline-category logit model.
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Metadata
- Authors
- Hartzel, J., Agresti, A., & Caffo, B.
- Venue
- Statistical Modelling
- Publisher
- SAGE Publications
- Published
- 2001-07
- Volume
- 1
- Issue
- 2
- Pages
- 81-102
- ISSN
- 1471-082X (ISSN-L)
- 1477-0342
- OpenAlex
- W2164985658
- Cited by
- 200
- References
- 0
- FWCI
- 2.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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