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.
Original language | English (US) |
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Pages (from-to) | 81-102 |
Number of pages | 22 |
Journal | Statistical Modeling |
Volume | 1 |
Issue number | 2 |
DOIs | |
State | Published - Jul 2001 |
Externally published | Yes |
Keywords
- adjacent-categories logit
- baseline-category logit
- generalized linear mixed model
- nominal variable
- non-parametric maximum likelihood
- ordinal variable
- quasi symmetry
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty