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Disease resistance modelled as first‐passage times of genetically dependent stochastic processes

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  • S. Sæbø
  • T. Almøy
  • A. H. Aastveit

Abstract

Summary. Mastitis resistance data on dairy cattle are modelled as first‐passage times of stochastic processes. Population heterogeneity is included by expressing process parameters as functions of shared random variables. We show how dependences between individuals, e.g. genetic relationships, can be exploited in the analyses. The method can be extended to handle situations with multiple hidden causes of failure. Markov chain Monte Carlo methods are used for estimation.

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  • S. Sæbø & T. Almøy & A. H. Aastveit, 2005. "Disease resistance modelled as first‐passage times of genetically dependent stochastic processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 273-285, January.
    • Handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:273-285
    DOI: 10.1111/j.1467-9876.2005.00483.x
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    Cited by:

    1. Mei-Ling Ting Lee & George A. Whitmore, 2022. "Multivariate Threshold Regression Models with Cure Rates: Identification and Estimation in the Presence of the Esscher Property," Stats, MDPI, vol. 5(1), pages 1-18, February.

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