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Parametric regression models for continuous time removal and recapture studies
Author(s) -
Lin D. Y.,
Yip P. S. F.
Publication year - 1999
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00184
Subject(s) - statistics , covariate , estimator , mathematics , parametric statistics , mark and recapture , regression analysis , missing data , population , sample size determination , regression , martingale (probability theory) , econometrics , censored regression model , demography , sociology
We use a class of parametric counting process regression models that are commonly employed in the analysis of failure time data to formulate the subject‐specific capture probabilities for removal and recapture studies conducted in continuous time. We estimate the regression parameters by modifying the conventional likelihood score function for left‐truncated and right‐censored data to accommodate an unknown population size and missing covariates on uncaptured subjects, and we subsequently estimate the population size by a martingale‐based estimating function. The resultant estimators for the regression parameters and population size are consistent and asymptotically normal under appropriate regularity conditions. We assess the small sample properties of the proposed estimators through Monte Carlo simulation and we present an application to a bird banding exercise.