Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models

  title={Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models},
  author={Hans J. Skaug and David A. Fournier},
  journal={Computational Statistics & Data Analysis},
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… CONTINUE READING
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Negative binomial loglinear mixed models

  • N. E. Breslow, X. Lin
  • Statist . Modelling
  • 2003

Automatic differentiation to facilitate maximum likelihood estimation in nonlinear random effects models

  • L. Tierney, J. B. Kadane
  • J . Comput . Graphical Statist .
  • 2002

An introduction to AD MODEL BUILDER Version 6.0.2 for use in nonlinear modeling and statistics

  • D. Fournier
  • 2001
1 Excerpt

Maximum likelihood for generalized linear models with nested random effects via high - order , multivariate Laplace approximation

  • D. E. Rumelhart, G. E. Hinton, R. J. Williams
  • J . Comput . Graphical Statist .
  • 2000

Laplace importance sampling for generalized linear mixed models

  • A. Y. C. Kuk, Y. W. Cheng
  • J . Statist . Comput . Simulation
  • 1999
2 Excerpts

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