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Computational Aspects of Likelihood-Based Inference for Variance Components

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Advances in Statistical Methods for Genetic Improvement of Livestock

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

Abstract

In this paper, iterative algorithms for computing restricted maximum likelihood (REML) estimates of variance components are discussed in the context of a possibly unbalanced, mixed linear model that contains a single set of m random effects. The coverage includes the Newton-Raphson algorithm, the method of scoring, the EM algorithm, and the method of successive approximations.

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Author information

Authors and Affiliations

  1. Department of Statistics, Iowa State University, Ames, Iowa, USA

    D. A. Harville

  2. Applied Statistics, Management Services Division, Eastman Kodak Company, Rochester, New York, USA

    T. P. Callanan

Authors
  1. D. A. Harville
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  2. T. P. Callanan
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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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Harville, D.A., Callanan, T.P. (1990). Computational Aspects of Likelihood-Based Inference for Variance Components. 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_8

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  • DOI: https://doi.org/10.1007/978-3-642-74487-7_8

  • 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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