BibTeX
@ARTICLE{
         Maunder2008Coe,
       title = "Comparison of estimators for mark-recapture models: random effects, hierarchical
         Bayes, and AD Model Builder",
       author = "M.N. Maunder, H.J. Skaug, D.A. Fournier, S.D. Hoyle",
       year = "2008",
       journal = "Environmental and Ecological Statistics",
       volume = "3",
       pages = "917--948",
       abstract = "Mark-recapture studies are one of the most common methods used to obtain
         demographic parameters for wildlife populations. Time specific estimates of parameters representing
         population processes contain both temporal variability in the process (process error) and error in
         estimating the parameters (observation error). Therefore, to estimate the temporal variation in the
         population process, it is important to separate these two errors. Traditional random effect models
         can be used to separate the two errors. However, it is difficult to implement the required
         simultaneous maximization and integration for dynamic nonlinear non-Gaussian models. An alternative
         hierarchical Bayesian approach using MCMC integration is easier to apply, but requires priors for
         all model parameters. AD Model Builder (ADMB) is a general software environment for fitting
         parameter rich nonlinear models to data. It uses automatic differentiation to provide a more
         efficient and stable parameter estimation framework. ADMB has both random effects using Laplace
         approximation and importance sampling, and MCMC to implement Bayesian analysis. To demonstrate ADMB
         and investigate methods to analyze mark-recapture data, we implement fixed effect, random effect,
         and hierarchical Bayes estimators in ADMB and apply them to three mark-recapture data sets. Our
         results showed that unrestricted time-effects, random effects, and hierarchical Bayes methods often
         give similar results, but not in all cases or for all parameters.",
       ad_area = "Biology",
       ad_tools = "AD Model Builder"
}