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Average information residual maximum likelihood in practice.

Arthur R Gilmour1

  • 1Orange, New South Wales, Australia.

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|June 28, 2019
PubMed
Summary
This summary is machine-generated.

The average information residual maximum likelihood (REML) algorithm efficiently estimates variance parameters in linear mixed models. This paper details its application to general models and ASReml software, addressing key fitting challenges.

Keywords:
ASRemlEchidna softwareREMLestimationmixed modelvariance component

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Area of Science:

  • Statistics
  • Computational Biology
  • Quantitative Genetics

Background:

  • The average information residual maximum likelihood (REML) algorithm was introduced for variance parameter estimation in linear mixed models.
  • Initial applications focused on variance component models but expanded to more general models.
  • The algorithm is implemented in statistical software packages like ASReml.

Purpose of the Study:

  • To outline the theory of the average information REML algorithm for general linear mixed models.
  • To describe challenges encountered when fitting these models.
  • To explain how ASReml software addresses these challenges.

Main Methods:

  • Implementation steps of the average information REML algorithm.
  • Techniques for maintaining parameters within the valid parameter space.
  • Strategies for maximizing computational efficiency through sparsity.

Main Results:

  • The paper details the theoretical underpinnings for applying REML to general models.
  • It addresses practical issues such as parameter space constraints and matrix singularities.
  • Factor-analytic structures are presented as a method to handle unstructured variance matrices.

Conclusions:

  • The average information REML algorithm, as implemented in ASReml, provides a robust framework for fitting complex linear mixed models.
  • Addressing issues like parameter space boundaries and matrix singularities is crucial for reliable variance estimation.
  • Ongoing work focuses on further refining the algorithm and its implementation.