BibTeX
@ARTICLE{
         Skaug2006Aao,
       title = "Automatic approximation of the marginal likelihood in nonlinear hierarchical models",
       author = "Skaug, H. and Fournier, D",
       journal = "Computational Statistics and Data Analysis",
       pages = "699--709",
       abstract = "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.",
       ad_area = "Optimization",
       ad_tools = "AD Model Builder",
       ad_theotech = "Adjoint, Hessian, Integration of Analytic Derivatives, Reverse Mode",
       year = "2006",
       volume = "51",
       number = "2",
       doi = "10.1016/j.csda.2006.03.005"
}