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Activity Number: 139
Type: Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #315580 View Presentation
Title: Estimation in Closed Capture-Recapture Models with Missing Covariate Data
Author(s): Shen-Ming Lee* and Wen-Han Hwang and Jean de Dieu Tapsoba
Companies: Feng Chia University and National Chung Hsing University and Fred Hutchinson Cancer Research Center
Keywords: Inverse probability weighting ; Missing at random ; Multiple imputation; ; Population size estimation ; Regression calibration

Individual covariates are commonly used in a capture-recapture model as they can provide important information for the population size estimation. However, in practical applications, some covariates may be missing and that can lead to unreliable inference if the records with missing data were just ignored. This study considers the estimation problems when some covariates are missing at random. When some covariates are missing, the naive complete-case approach is shown to underestimate the population size. We develop methods for estimating regression parameters and population size based on regression calibration, inverse probability weighting and multiple imputation techniques without any distributional assumption about the covariates. A simulation study was carried out to investigate the effects of missing covariates and to evaluate the performance of our proposed methods. A data of bird species yellow-bellied prinia collected in Hong Kong was analyzed for illustration.

Authors who are presenting talks have a * after their name.

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