Making REML computationally feasible for large data sets: use of the Gibbs sampler

DOI10.1080/0094965031000110588zbMath1048.62068OpenAlexW2056168563MaRDI QIDQ4825498

David A. Harville

Publication date: 28 October 2004

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/0094965031000110588

zbMATH Keywords

EM algorithmvariance componentsGibbs samplermethod of successive approximationsrestricted maximum likelihood estimationmixed-effects linear models

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (2)

Assessing the performance of normal-based and REML-based confidence intervals for the intraclass correlation coefficient ⋮ Nonparametric bootstrap confidence intervals for variance components applied to interlaboratory comparisons

Cites Work

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• Sampling-Based Approaches to Calculating Marginal Densities
• Statistical and Computational Aspects of Mixed Model Analysis
• Computation of variance components using the em algorithm
• Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
• The Estimation of Environmental and Genetic Trends from Records Subject to Culling
• Maximum-likelihood estimation for the mixed analysis of variance model
• Recovery of inter-block information when block sizes are unequal

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