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.
Bayesian latent class models for capture–recapture in the presence of missing data / Di Cecco, D.; Di Zio, M.; Liseo, B.. - In: BIOMETRICAL JOURNAL. - ISSN 0323-3847. - (2020). [10.1002/bimj.201900111]
Bayesian latent class models for capture–recapture in the presence of missing data
Di Cecco D.
;Di Zio M.;Liseo B.
2020
Abstract
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.File allegati a questo prodotto
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