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Related Experiment Videos

The PX-EM algorithm for fast stable fitting of Henderson's mixed model.

J L Foulley1, D A van Dyk

  • 1Station de génétique quantitative et appliquée, Institut national de la recherche agronomique, 78352 Jouy-en-Josas Cedex, France. foulley@jouy.inra.fr

Genetics, Selection, Evolution : GSE
|January 23, 2004
PubMed
Summary

This study introduces the PX-EM algorithm for estimating variance components in mixed models, showing improved convergence over the standard EM algorithm for random regression. This enhances analysis of complex genetic and longitudinal data.

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Area of Science:

  • Statistics
  • Quantitative Genetics
  • Biometry

Background:

  • Henderson's linear mixed models are crucial for analyzing complex data structures.
  • Estimating variance covariance components accurately is essential for model validity.
  • Existing algorithms like the Expectation-Maximization (EM) algorithm can face convergence challenges.

Purpose of the Study:

  • To present procedures for implementing the PX-EM algorithm.
  • To compute Restricted Maximum Likelihood (REML) estimates of variance covariance components.
  • To improve convergence characteristics compared to the basic EM algorithm.

Main Methods:

  • Implementation of the PX-EM algorithm by Liu, Rubin, and Wu.
  • Application to linear mixed models with correlated random factors of equal vector length.

Related Experiment Videos

  • Testing on random regression models and sire-maternal grandsire models.

    • Main Results:

    • The PX-EM algorithm provides effective REML estimates for variance covariance components.
    • Demonstrated superior convergence characteristics (fewer iterations, less time) compared to the basic EM algorithm.
    • Successful application illustrated through numerical examples.

    Conclusions:

    • The PX-EM algorithm offers a more efficient computational approach for variance component estimation in mixed models.
    • This method is particularly beneficial for complex models like those in longitudinal data analysis and genetic evaluation.
    • The findings suggest PX-EM as a valuable tool for researchers in statistics and quantitative genetics.