The use of mixture models for estimating the size of an elusive population when capture rates vary among individuals has received strong attention from researchers. In this paper we propose a Bayesian semi-parametric approach by considering a truncated infinite dimensional Poisson mixture model for capture recapture count data. An application in official statistics regarding the estimate of the size of criminal populations is used to illustrate the proposed methodology.

Population Size Estimation by Repeated Identifications of Units. A Bayesian Semi-parametric Mixture Model Approach / Tuoto, Tiziana; DI CECCO, Davide; Tancredi, Andrea. - (2023), pp. 425-434. - SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS.

Population Size Estimation by Repeated Identifications of Units. A Bayesian Semi-parametric Mixture Model Approach

Tuoto Tiziana;Di Cecco Davide;Tancredi Andrea
2023

Abstract

The use of mixture models for estimating the size of an elusive population when capture rates vary among individuals has received strong attention from researchers. In this paper we propose a Bayesian semi-parametric approach by considering a truncated infinite dimensional Poisson mixture model for capture recapture count data. An application in official statistics regarding the estimate of the size of criminal populations is used to illustrate the proposed methodology.
2023
Studies in Theoretical and Applied Statistics
9783031166082
Criminal populations; Capture-recapture; Dirichlet process mixture; Official statistics
02 Pubblicazione su volume::02a Capitolo o Articolo
Population Size Estimation by Repeated Identifications of Units. A Bayesian Semi-parametric Mixture Model Approach / Tuoto, Tiziana; DI CECCO, Davide; Tancredi, Andrea. - (2023), pp. 425-434. - SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1670248
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