Bayesian latent class models for capture–recapture in the presence of missing data
01 Pubblicazione su rivista
Di Cecco D., Di Zio M., Liseo B.
ISSN: 0323-3847
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.