A new Bayesian method for nonparametric capture-recapture models in presence of heterogeneity

• 成果类型: Article
• 署名作者: Tardella, L
• 署名单位: Sapienza University Rome
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/89.4.807
• 发表日期: 2002
• 页码: 807817
• 关键词: estimating population-size; probabilities vary; Robust Estimation; moment sequences; reference priors; animals

摘要

The intrinsic heterogeneity of individuals is a potential source of bias in estimation procedures for capture-recapture models. To account for this heterogeneity in the model a hierarchical structure has been proposed whereby the probabilities that each animal is caught on a single occasion are modelled as independent draws from a common unknown distribution F. However, there is general agreement that modelling F by a simple parametric curve may lead to unsatisfactory results. Here we propose an alternative Bayesian approach that relies on a different parameterisation which imposes no assumption on the shape of F but drives the problem back to a finite-dimensional setting. Our approach avoids some identifiability issues related to such a recapture model while allowing for a formal Bayesian default analysis. Results of analyses of computer simulations and of real data show that the method performs well.