title: Bayesian modeling of age-specific survival in nesting studies under Dirichlet priors.
abstract: There has been much work done in nest survival analysis using the maximum likelihood (ML) method. The ML method suffers from the instability of numerical calculations when models having a large number of unknown parameters are used. A Bayesian approach of model fitting is developed to estimate age-specific survival rates for nesting studies using a large class of prior distributions. The computation is done by Gibbs sampling. Some latent variables are introduced to simplify the full conditional distributions. The method is illustrated using both a real and a simulated data set. Results indicate that Bayesian analysis provides stable and accurate estimates of nest survival rates.
authors.affiliation: Department of Statistics, University of Missouri-Columbia, 65211, USA. chong@stat.missouri.edu
authors.forename: C Z
authors.initials: CZ
authors.lastname: He
authors.forename: D
authors.initials: D
authors.lastname: Sun
authors.forename: Y
authors.initials: Y
authors.lastname: Tra
mesh_terms: D000367:Age Factors; D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D001717:Birds / Q000502:physiology*; D005260:Female; D016013:Likelihood Functions; D008297:Male; D012098:Reproduction; D020308:Songbirds / Q000502:physiology; D015996:Survival Rate