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- Title
Generalised additive mixed models analysis via gammSlice.
- Authors
Pham, Tung H.; Wand, Matt P.
- Abstract
Summary: We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.
- Subjects
MARKOV processes; MULTIVARIATE analysis; NUMERICAL solutions to functional equations; BAYESIAN analysis; MONTE Carlo method
- Publication
Australian & New Zealand Journal of Statistics, 2018, Vol 60, Issue 3, p279
- ISSN
1369-1473
- Publication type
Article
- DOI
10.1111/anzs.12241