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Original Articles

Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model

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Pages 31-38 | Published online: 09 Apr 2012

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Marcus B. Perry & Joseph J. Pignatiello$suffix/text()$suffix/text(). (2012) Identifying the time of change in the mean of a two-stage nested process. Journal of Applied Statistics 39:2, pages 419-433.
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Honghu Liu, Yan Zheng & Jie Shen. (2008) Goodness-of-fit measures of R 2 for repeated measures mixed effect models. Journal of Applied Statistics 35:10, pages 1081-1092.
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Renger H. Jellema, David J. Louwerse, Age K. Smilde, Mathieu J. P. Gerritsen, Daan Guldemond, Hilko van der Voet & Pieter F. G. Vereijken. (2003) A Systematic Quantification of the Sources of Variation of Process Analytical Measurements in the Steel Industry. Quality Engineering 15:3, pages 391-402.
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Sharon L. Lohr & Michelle Divan. (1997) Comparison of confidence intervals for variance components with unbalanced data. Journal of Statistical Computation and Simulation 58:1, pages 83-97.
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Alex. Hrong-Tai Fai & Paul L. Cornelius. (1996) Approximate F-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments. Journal of Statistical Computation and Simulation 54:4, pages 363-378.
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Bilgehan Güven. (1995) Maximum likelihood estimation in simple linear regression with one fold nested error. Communications in Statistics - Theory and Methods 24:1, pages 121-130.
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Rik C. Schoemaker & Hans C. van Houwelingen. (1994) Repeated measures for two within-subject factors: analysis and missing data solutions. Journal of Biopharmaceutical Statistics 4:2, pages 173-188.
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CarlJ. Schwarz. (1993) The Mixed-Model ANOVA: The Truth, the Computer Packages, the Books. The American Statistician 47:1, pages 48-59.
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Lee-Ing Tong & P.L. Cornelius. (1989) Studies on the estimation of the slope parameter in the simple linear regression model with one-fold nested error structure. Communications in Statistics - Simulation and Computation 18:1, pages 201-225.
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Jang Taek Lee & Byung Chun Kim. (1988) A New Approach for the W-Matrix. Journal of Statistical Computation and Simulation 29:3, pages 241-254.
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WilliamH. Swallow & JohnF. Monahan. (1984) Monte Carlo Comparison of ANOVA, MIVQUE, REML, and ML Estimators of Variance Components. Technometrics 26:1, pages 47-57.
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R. D Anderson, H. V Henderson, F Pukelsheim & S. R Searle. (1984) Best estimation of variance components from balanced data, with arbitrary kurtosis. Series Statistics 15:2, pages 163-176.
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F. G. Giesbrecht. (1983) An efficient procedure for computing minque of variance components and generalized least squares estimates of fixed effects. Communications in Statistics - Theory and Methods 12:18, pages 2169-2177.
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J.H. Goodnight & W.J. Hemmerle. (1979) A Simplified Algorithm for the W Transformation in Variance Component Estimation. Technometrics 21:2, pages 265-267.
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John J. Miller. (1979) Maximum likelihood estimation of variance components-a Monte Carlo study. Journal of Statistical Computation and Simulation 8:3-4, pages 175-190.
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Francis G. Giesbrecht & Peter M. Burrows. (1978) Estimating variance components in hiearchical structures using minque and restricted maximum livelihood. Communications in Statistics - Theory and Methods 7:9, pages 891-904.
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Lon-Mu Liu & Jerome Senturia. (1977) Computation of MINQUE Variance Component Estimates. Journal of the American Statistical Association 72:360a, pages 867-868.
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DavidA. Harville. (1977) Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems. Journal of the American Statistical Association 72:358, pages 320-338.
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Friedrich Pukelsheim. (1977) On hsu's model in regression analysis. Series Statistics 8:3, pages 323-331.
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