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Random effects and shrinkage estimation in capture-recapture models

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  • J. Andrew Royle
  • William Link

Abstract

We discuss the analysis of random effects in capture-recapture models, and outline Bayesian and frequentists approaches to their analysis. Under a normal model, random effects estimators derived from Bayesian or frequentist considerations have a common form as shrinkage estimators. We discuss some of the difficulties of analysing random effects using traditional methods, and argue that a Bayesian formulation provides a rigorous framework for dealing with these difficulties. In capture-recapture models, random effects may provide a parsimonious compromise between constant and completely time-dependent models for the parameters (e.g. survival probability). We consider application of random effects to band-recovery models, although the principles apply to more general situations, such as Cormack-Jolly-Seber models. We illustrate these ideas using a commonly analysed band recovery data set.

Suggested Citation

  • J. Andrew Royle & William Link, 2002. "Random effects and shrinkage estimation in capture-recapture models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 329-351.
    • Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:329-351
    DOI: 10.1080/02664760120108746
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    References listed on IDEAS

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    1. V. Chavez-Demoulin, 1999. "Bayesian Inference for Small-Sample Capture-Recapture Data," Biometrics, The International Biometric Society, vol. 55(3), pages 727-731, September.
    2. S. P. Brooks & E. A. Catchpole & B. J. T. Morgan & S. C. Barry, 2000. "On the Bayesian Analysis of Ring-Recovery Data," Biometrics, The International Biometric Society, vol. 56(3), pages 951-956, September.
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    Cited by:

    1. S. C. Barry & S. P. Brooks & E. A. Catchpole & B. J. T. Morgan, 2003. "The Analysis of Ring-Recovery Data Using Random Effects," Biometrics, The International Biometric Society, vol. 59(1), pages 54-65, March.

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