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A new method for analysing discrete life history data with missing covariate values

Author

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  • E. A. Catchpole
  • B. J. T. Morgan
  • G. Tavecchia

Abstract

Summary. Regular censusing of wild animal populations produces data for estimating their annual survival. However, there can be missing covariate data; for instance time varying covariates that are measured on individual animals often contain missing values. By considering the transitions that occur from each occasion to the next, we derive a novel expression for the likelihood for mark–recapture–recovery data, which is equivalent to the traditional likelihood in the case where no covariate data are missing, and which provides a natural way of dealing with covariate data that are missing, for whatever reason. Unlike complete‐case analysis, this approach does not exclude incompletely observed life histories, uses all available data and produces consistent estimators. In a simulation study it performs better overall than alternative methods when there are missing covariate data.

Suggested Citation

  • E. A. Catchpole & B. J. T. Morgan & G. Tavecchia, 2008. "A new method for analysing discrete life history data with missing covariate values," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 445-460, April.
    • Handle: RePEc:bla:jorssb:v:70:y:2008:i:2:p:445-460
    DOI: 10.1111/j.1467-9868.2007.00644.x
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    Cited by:

    1. Simon J. Bonner & Byron J. T. Morgan & Ruth King, 2010. "Continuous Covariates in Mark-Recapture-Recovery Analysis: A Comparison of Methods," Biometrics, The International Biometric Society, vol. 66(4), pages 1256-1265, December.
    2. Jakub Stoklosa & Wen-Han Hwang & Sheng-Hai Wu & Richard Huggins, 2011. "Heterogeneous Capture–Recapture Models with Covariates: A Partial Likelihood Approach for Closed Populations," Biometrics, The International Biometric Society, vol. 67(4), pages 1659-1665, December.
    3. Simon J. Bonner & Wei Zhang & Jiaqi Mu, 2024. "On the identifiability of the trinomial model for mark‐recapture‐recovery studies," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    4. Stoklosa, Jakub & Dann, Peter & Huggins, Richard M. & Hwang, Wen-Han, 2016. "Estimation of survival and capture probabilities in open population capture–recapture models when covariates are subject to measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 74-86.
    5. Shen‐Ming Lee & Wen‐Han Hwang & Jean de Dieu Tapsoba, 2016. "Estimation in closed capture–recapture models when covariates are missing at random," Biometrics, The International Biometric Society, vol. 72(4), pages 1294-1304, December.
    6. Blanca Sarzo & Ruth King & David Conesa & Jonas Hentati-Sundberg, 2021. "Correcting Bias in Survival Probabilities for Partially Monitored Populations via Integrated Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 200-219, June.

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