JSM 2005 - Toronto

Abstract #303074

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Activity Number: 396
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303074
Title: A Bayesian Treed Approach to Form Poststrata for Capture-Recapture Data
Author(s): Xinlei Wang*+ and Johan Lim and Lynne Stokes
Companies: Southern Methodist University and Texas A&M University and Southern Methodist University
Address: Department of Statistical Science, Dallas, TX, 75275-0332, United States
Keywords: Binary trees ; Bayesian model selection ; Closed population ; Heterogeneity
Abstract:

For the problem of population size estimation in a typical two-period recapture experiment, Sekar and Deming (1949) suggested a method to account for heterogeneity of capture probabilities. Their approach is to divide the captured individuals into poststrata believed to be more homogeneous with respect to capture probabilities than the entire population, then make separate estimates of population sizes within the poststrata and sum them. However, there is little guidance in the literature about how to choose a poststratification scheme. In this paper, we propose a Bayesian Treed Capture-Recapture Model (CRM) to provide a systematic and effective way to form poststrata for the Sekar and Deming estimator. The attractive features of the proposed models include reduction of correlation bias, robustness, practical flexibility, simplicity, and interpretability. We compare the performance of estimators based on this approach to those of alternative models in several examples.

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