Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 51, Issue 2, Pages 699-709Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2006.03.005
Keywords
AD model builder; automatic differentiation; importance sampling; Laplace approximation; mixed models; random effects
Ask authors/readers for more resources
Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated by the program BUGS. The word automatic means that the technical details of computation are made transparent to the user. This is achieved by combining a technique from computer science known as automatic differentiation with the Laplace approximation for calculating the marginal likelihood. Automatic differentiation, which should not be confused with symbolic differentiation, is mostly unknown to statisticians, and hence basic ideas and results are reviewed. The computational performance of the approach is compared to that of existing mixed-model software on a suite of datasets selected from the mixed-model literature. (c) 2006 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available