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Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
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1977
Year
Parameter EstimationEngineeringLocalizationMaximum Likelihood ApproachesMixture AnalysisBiostatisticsPublic HealthEstimation TheoryPrincipal Component AnalysisStatisticsMaximum LikelihoodVariance ComponentsDensity EstimationEstimation StatisticFunctional Data AnalysisRelated ProblemsMl EstimatorsVariance Component EstimationStatistical InferenceMultivariate Analysis
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.