Variance components estimation in mixed linear model—the sub-diagonalization method

A. Silva, M. Fonseca, J. Mexia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

This work aims to introduce a new method of estimating the variance components in mixed linear models. The approach will be done firstly for models with 3 variances components and secondly attention will be devoted to general case of models with an arbitrary number of variance components. In our approach, we construct and apply a finite sequence of orthogonal matrices to the mixed linear model variance-covariance structure in order to produce a set of Gauss–Markov sub-models which will be used to create pooled estimators for the variance components. Numerical results will be given, comparing the performance of our proposed estimator to the one based on likelihood procedure.

Original languageEnglish
Title of host publicationApplied and Computational Matrix Analysis - MAT-TRIAD, Selected, Revised Contributions
PublisherSpringer New York LLC
Pages317-341
Number of pages25
Volume192
ISBN (Print)9783319499826
DOIs
Publication statusPublished - 1 Jan 2017
EventInternational Conference on Matrix Analysis and its Applications, MAT-TRIAD 2015 - Coimbra, Portugal
Duration: 7 Sep 201511 Sep 2015

Conference

ConferenceInternational Conference on Matrix Analysis and its Applications, MAT-TRIAD 2015
Country/TerritoryPortugal
City Coimbra
Period7/09/1511/09/15

Keywords

  • Mixed linear model
  • Orthogonal matrices
  • Simultaneous diagonalization
  • Variance components

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