Continuous covariates in mark-recapture-recovery analysis: A comparison of methods

Bonner, Simon J. and Morgan, Byron J. T. and King, Ruth (2010) Continuous covariates in mark-recapture-recovery analysis: A comparison of methods. Biometrics, 66 (4). pp. 1256-1265. ISSN 0006-341X. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Time varying, individual covariates are problematic in experiments with marked animals because the covariate can typically only be observed when each animal is captured. We examine three methods to incorporate time varying, individual covariates of the survival probabilities into the analysis of data from mark-recapture-recovery experiments: deterministic imputation, a Bayesian imputation approach based on modeling the joint distribution of the covariate and the capture history, and a conditional approach considering only the events for which the associated covariate data are completely observed (the trinomial model). After describing the three methods, we compare results from their application to the analysis of the effect of body mass on the survival of Soay sheep (Ovis aries) on the Isle of Hirta, Scotland. Simulations based on these results are then used to make further comparisons. We conclude that both the trinomial model and Bayesian imputation method perform best in different situations. If the capture and recovery probabilities are all high, then the trinomial model produces precise, unbiased estimators that do not depend on any assumptions regarding the distribution of the covariate. In contrast, the Bayesian imputation method performs substantially better when capture and recovery probabilities are low, provided that the specified model of the covariate is a good approximation to the true data-generating mechanism.

Item Type: Article
Uncontrolled keywords: Bayesian inference;Imputation;Individual covariates;Mark-recapture-recovery;Missing covariates;Time-varying continuous covariates;Trinomial model
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Byron Morgan
Date Deposited: 05 Oct 2012 11:56
Last Modified: 28 May 2014 08:45
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