title: Bayesian population size estimation using Dirichlet process mixtures.
abstract: We introduce a new Bayesian nonparametric method for estimating the size of a closed population from multiple-recapture data. Our method, based on Dirichlet process mixtures, can accommodate complex patterns of heterogeneity of capture, and can transparently modulate its complexity without a separate model selection step. Additionally, it can handle the massively sparse contingency tables generated by large number of recaptures with moderate sample sizes. We develop an efficient and scalable MCMC algorithm for estimation. We apply our method to simulated data, and to two examples from the literature of estimation of casualties in armed conflicts.
authors.affiliation: Department of Statistics, Indiana University, Bloomington, Indiana 47408, U.S.A.
authors.forename: Daniel
authors.initials: D
authors.lastname: Manrique-Vallier
mesh_terms: D000465:Algorithms; D000068337:Armed Conflicts / Q000706:statistics & numerical data; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D054527:Mass Casualty Incidents / Q000706:statistics & numerical data; D015233:Models, Statistical; D011156:Population Density; D018401:Sample Size; D018709:Statistics, Nonparametric
keywords: Capture-recapture; Casualties in conflicts; Dirichlet process mixtures; Latent class models; Model selection