Evaluating tools for capture-recapture model selection to estimate the size of hidden populations: it works in practice, but does it work in theory?

ABSTRACT:

Purpose

Capture-recapture methods estimate the size of hidden populations by leveraging the proportion of overlap of the population on independent lists. Log-linear modeling relaxes the assumption of list independence, but best model selection criteria remain uncertain. Incorrect model selection can deliver incorrect and even implausible size estimates.

Methods

We used simulations to model when capture-recapture methods deliver biased or unbiased estimates and compare model selection criteria. Simulations included five scenarios for list dependence among three incomplete lists of population of interest. We compared metrics of log-linear model selection, accuracy, and precision. We also compared log-linear model performance to the decomposable graph approach (a Bayesian model

SUBMITTER: Wesson P 

PROVIDER: S-EPMC9826711 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature