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Bayesian latent class models for capture–recapture in the presence of missing data
Author(s) -
Di Cecco Davide,
Di Zio Marco,
Liseo Brunero
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201900111
Subject(s) - missing data , latent class model , mark and recapture , bayesian probability , gibbs sampling , data set , class (philosophy) , statistics , computer science , set (abstract data type) , artificial intelligence , population , mathematics , data mining , pattern recognition (psychology) , demography , sociology , programming language
We propose a method for estimating the size of a population in a multiple record system in the presence of missing data. The method is based on a latent class model where the parameters and the latent structure are estimated using a Gibbs sampler. The proposed approach is illustrated through the analysis of a data set already known in the literature, which consists of five registrations of neural tube defects.