title: 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 at random 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. We show that the inverse probability weighting and multiple imputation approaches are asymptotically equivalent. We present a simulation study to investigate the effects of missing covariates and to evaluate the performance of the proposed methods. We also illustrate an analysis using data on the bird species yellow-bellied prinia collected in Hong Kong.
authors.affiliation: Department of Statistics, Feng Chia University, Taichung City, Taiwan.
authors.forename: Shen-Ming
authors.initials: SM
authors.lastname: Lee
authors.affiliation: Institute of Statistics, National Chung Hsing University, Taichung City, Taiwan.
authors.forename: Wen-Han
authors.initials: WH
authors.lastname: Hwang
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.
authors.forename: Jean
authors.initials: J
authors.lastname: de Dieu Tapsoba
mesh_terms: D000818:Animals; D001717:Birds; D003198:Computer Simulation; D000068598:Data Accuracy; D006723:Hong Kong; D006801:Humans; D015233:Models, Statistical; D011156:Population Density; D011336:Probability; D012044:Regression Analysis
keywords: Inverse probability weighting; Missing at random; Multiple imputation; Population size estimation; Regression calibration