Publication: Comparison of estimators for mark-recapture models: random effects, hierarchical Bayes, and AD Model Builder
Introduction
Applications
Tools
Research Groups
Workshops
Publications
   List Publications
   Advanced Search
   Info
   Add Publications
My Account
About

Comparison of estimators for mark-recapture models: random effects, hierarchical Bayes, and AD Model Builder

- Article in a journal -
 

Area
Biology

Author(s)
M. N. Maunder , H. J. Skaug , D. A. Fournier , S. D. Hoyle

Published in
Environmental and Ecological Statistics

Year
2008

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'>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 Tools
ad Model Builder'>ad Model Builder

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"
}


back
  

Contact:
autodiff.org
Username:
Password:
(lost password)