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Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems

David A. Harville
Journal of the American Statistical Association
Vol. 72, No. 358 (Jun., 1977), pp. 320-338
DOI: 10.2307/2286796
Stable URL: http://www.jstor.org/stable/2286796
Page Count: 19
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Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
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Abstract

Recent developments promise to increase greatly the popularity of maximum likelihood (ML) as a technique for estimating variance components. Patterson and Thompson (1971) proposed a restricted maximum likelihood (REML) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects. Miller (1973) developed a satisfactory asymptotic theory for ML estimators of variance components. There are many iterative algorithms that can be considered for computing the ML or REML estimates. The computations on each iteration of these algorithms are those associated with computing estimates of fixed and random effects for given values of the variance components.

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