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Activity Number:
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479
- Applied Bayesian Nonparametric Modeling
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #313736
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Title:
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Nonparametric Bayesian Population Size Estimation with Missing Entries
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Author(s):
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Dongah Kim* and Krista Gile
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Companies:
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University of Massachusetts, Amherst and University of Massachusetts, Amherst
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Keywords:
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Nonparametric;
Bayesian;
Capture-Recapture;
missing values;
mixture model
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Abstract:
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We propose a Bayesian nonparametric method for estimating closed population size based on multiple lists with missing entries. Based on the Bayesian population size estimation developed by \cite{Manrique2016}, we extended this approach when the missing entries exist. This approach, based on Dirichlet process mixture models, can capture the complex dependencies without assuming a specific number of latent classes. We deal with missing entries by using data-augmentation, jointly fitting Bayesian Capture-Recapture models and modeling the missing data process. We apply this new method in a simulation study with several scenarios and to data on casualties in the Syrian civil war.
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Authors who are presenting talks have a * after their name.
