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The evaluation of variance component estimation software: generating benchmark problems by exact and approximate methods

Author

Listed:
  • Jörg Wensch
  • Monika Wensch-Dorendorf
  • Hermann Swalve

Abstract

The prediction of breeding values depends on the reliable estimation of variance components. This complex task leads to nonlinear minimization problems that have to be solved by numerical algorithms. In order to evaluate the reliability of these algorithms benchmark problems have to be constructed where the exact solution is a priori known. We develop techniques to construct such benchmark problems for mixed models including fixed and random effects, ANOVA, ML and REML predictors, balanced and unbalanced data for 1-way classification. Besides the construction of artificial data that produce the desired variance components we describe a projection method to construct benchmark data from simulated data. We discuss the cases where exact expressions for the projection can be given and where a numerical approximation procedure has to be used. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Jörg Wensch & Monika Wensch-Dorendorf & Hermann Swalve, 2013. "The evaluation of variance component estimation software: generating benchmark problems by exact and approximate methods," Computational Statistics, Springer, vol. 28(4), pages 1725-1748, August.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:4:p:1725-1748
    DOI: 10.1007/s00180-012-0376-3
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    References listed on IDEAS

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    1. Shayle Searle, 1995. "An overview of variance component estimation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 42(1), pages 215-230, December.
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    More about this item

    Keywords

    Variance component estimation; Maximum likelihood; Restricted maximum likelihood; C53; C63;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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