Capture-recapture analysis with a latent class model allowing for local dependence and observed heterogeneity.

In epidemiology, capture-recapture models are commonly used to estimate the size of an unknown population based on several incomplete lists of individuals. The method operates under two main assumptions: independence between the lists (local independence) and homogeneity of capture probabilities of individuals. In practice, these assumptions are rarely satisfied. We introduce a multinomial latent class model that can account for both list dependence and heterogeneity. Parameter estimation is performed by maximizing the conditional likelihood function with the use of the EM algorithm. In addition, a new approach for evaluating the standard errors of the parameter estimates is discussed, which considerably reduces the computational burden associated with the evaluation of the variance of the population size estimate.