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Advances in Statistical Methods for Genetic Improvement of Livestock pp 344–360Cite as

Survival, Endurance and Censored Observations in Animal Breeding

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Part of the book series: Advanced Series in Agricultural Sciences ((AGRICULTURAL,volume 18))

Abstract

Longevity and other survival measures are important traits and these are usually subjected to censoring. Methods in failure time analysis, useful for the censored data encountered in animal breeding, are described. The value of the Cox model and of other rank regression models is highlighted. These methods incorporate an unknown transformation in the model, solving the scale problems frequently confronted in genetic analysis. Bayesian approaches allow models to accommodate random factors such as additive genetic values. Effects are estimated by solving, perhaps several times, a linear system of equations that resembles Henderson’s mixed model equations. Proposed numerical techniques are tested on an actual data set.

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Author information

Authors and Affiliations

  1. Animal Genetics and Breeding Unit, University of New England, Armidale, Australia

    S. P. Smith

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  1. S. P. Smith
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Editor information

Editors and Affiliations

  1. Department of Animal Sciences 126 Animal Sciences Laboratory, University of Illinois, 1207 West Gregory Drive, Urbana, Illinois, 61801, USA

    Daniel Gianola

  2. Animal Genetics and Breeding Unit (AGBU), University of New England and NSW Agriculture and Fisheries, Armidale, New South Wales, 2351, Australia

    Keith Hammond

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© 1990 Springer-Verlag Berlin Heidelberg

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Smith, S.P. (1990). Survival, Endurance and Censored Observations in Animal Breeding. In: Gianola, D., Hammond, K. (eds) Advances in Statistical Methods for Genetic Improvement of Livestock. Advanced Series in Agricultural Sciences, vol 18. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-74487-7_16

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  • DOI: https://doi.org/10.1007/978-3-642-74487-7_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-74489-1

  • Online ISBN: 978-3-642-74487-7

  • eBook Packages: Springer Book Archive

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