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Disease resistance modelled as first‐passage times of genetically dependent stochastic processes
Author(s) -
Sæbø S.,
Almøy T.,
Aastveit A. H.
Publication year - 2005
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2005.00483.x
Subject(s) - markov chain monte carlo , markov chain , stochastic process , monte carlo method , population , resistance (ecology) , statistical physics , mastitis , stochastic modelling , computer science , econometrics , mathematics , statistics , genetics , biology , medicine , physics , ecology , environmental health , microbiology and biotechnology
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.