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Activity Number: 624
Type: Invited
Date/Time: Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract #310764 View Presentation
Title: Bayesian Multiple-Recapture Estimation of Casualties in Armed Conflicts Using Nonparametric Mixtures
Author(s): Daniel Manrique-Vallier*+
Companies: Indiana University
Keywords: Capture-Recapture ; Bayesian ; Dirichlet Process ; Casualty estimation ; Conflicts

Beginning with the pioneering work of Patrick Ball in Guatemala in 1999, Multiple-Recapture (MR) techniques have become a popular tool for estimating total numbers of casualties in armed conflicts from multiple incomplete lists. A major challenge in these applications is to correctly account for individual heterogeneity of capture, and dependence between lists. Classic log-linear modeling is often a simple and reasonable approach. However, difficult problems such as model selection and low tolerance to sparsity when dealing with large numbers of lists, limit their broader applicability, and often require ad-hoc solutions. In this talk I present a new full Bayesian method, based on Dirichlet process mixtures. This method offers a principled way of accounting for complex patterns of heterogeneity of capture, obviating the need for a separate model selection process, and is computationally efficient. Additionally it has a high tolerance for sparsity. I illustrate it using historical data from conflicts in Kosovo and Peru.

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