Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models
- Computational Statistics, 15:3, 391-420
We use simulation studies (a) to compare Bayesian and likelihood fitting methods, in terms of validity of conclusions, in two-level random-slopes regression (RSR) models, and (b) to compare several Bayesian estimation methods based on Markov chain Monte Carlo, in terms of computational efficiency, in random-effects logistic regression (RELR) models. We find (a) that the Bayesian approach with a particular choice of diffuse inverse Wishart prior distribution for the (co)variance parameters performs at least as well-in terms of bias of estimates and actual coverage of nominal 95% intervals-as maximum likelihood methods in RSR models with medium sample sizes (expressed in terms of the number J of level-2 units), but neither approach performs as well as might be hoped wit small J; and (b) that an adaptive hybrid Metropolis-Gibbs sampling method we have developed for use in the multilevel modeling package MlwiN outperforms adaptive rejection Gibbs sampling in the RELR models we have considered, sometimes by a wide margin.
- Number of levels
- Model data structure
- Response types
- Multivariate response model?
- Longitudinal data?
- Further model keywords
- Substantive discipline
- Substantive keywords
Paper looks at choice of default priors in RSR models and also contains details of adapting Metropolis approach
- Paper submitted by
- William Browne, Bristol Veterinary School, University of Bristol, email@example.com