Estimation in closed capture-recapture models when covariates are missing at random

Abstract: 

Individual covariates are commonly used in capture-recapture models as they can provide important information for population size estimation. However, in practice, one or more covariates may be missing for some individuals, which can lead to unreliable inference if records with missing data are treated as missing completely at random. We show that, in general, such a naive complete-case analysis in closed capture-recapture models with some covariates missing at random underestimates the population size. We develop methods for estimating regression parameters and population size using regression calibration, inverse probability weighting, and multiple imputation without any distributional assumptions about the covariates. A simulation study was carried out to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. A dataset of the bird species yellow-bellied prinia collected in Hong Kong was analyzed for illustration. This is a joint work with Shen-Ming Lee and Jean de Dieu Tapsoba.