Bayesian latent class models for capture–recapture in the presence of missing data

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.

Multiple System Estimation using Covariates having Missing Values and Measurement Error: Estimating the Size of the Māori Population in New Zealand

“…(2007), and related work is found in di Cecco et al. (2018), van der Heijden and Smith (2020), and di Cecco et al (2020).…”

“…We also show under what conditions this is true in general. An alternative modelling approach is presented bySutherland et al (2007), and related work is found in diCecco et al (2018), van der Heijden and Smith (2020), and diCecco et al (2020).…”

Finite mixtures in capture–recapture surveys for modeling residency patterns in marine wildlife populations

“…Capture–Recapture (CR) methods are statistical techniques widely employed to estimate the size of an elusive population for which it is impossible to get a complete enumeration. This task, applied initially to ecology for the study of fish and wildlife populations (Matechou & Argiento, 2023; Otis et al., 1978; Wu & Holan, 2017), is now common to many other application fields such as epidemiology (Böhning et al., 2020; Chao et al., 2001; Maruotti et al., 2023) and social sciences (Böhning et al., 2018; Böhning & van der Heijden, 2009; Brittain & Böhning, 2009; Di Cecco et al., 2020; Farcomeni, 2022; Silverman, 2020). The term capture is inherited from the traditional way wild animals have been identified for decades—namely, through capture, marking, and release—but it is not necessarily intended for its physical sense anymore.…”

A Bayesian latent class model for predicting gestational age in health administrative data

“…Another attractive feature of a mixture approach, often called latent class models (LCMs) is the latent classification of individuals. Finite mixture models are widely adopted as a device in the literature to characterize the latent classes in LCMs 15–19 . Bayesian methods allow for a convenient estimation procedure and facilitate the interpretation of the inferential results, including posterior estimates of latent subgroup features, 20 class‐specific regression parameters, and subject‐level characteristics associated with class allocation.…”

“…Finite mixture models are widely adopted as a device in the literature to characterize the latent classes in LCMs. [15][16][17][18][19] Bayesian methods allow for a convenient estimation procedure and facilitate the interpretation of the inferential results, including posterior estimates of latent subgroup features, 20 classspecific regression parameters, and subject-level characteristics associated with class allocation. A previous study has shown that with a common set of predictors, standard LRMs produced inconsistencies in predictive performance and parameter estimates between term and preterm deliveries.…”