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Restricted maximum likelihood estimation under Eisenhart model Ill
Authors:KR Lee  CH Kapadia
Institution:Eastman Chemicals Company Kingsport TN 37662 U.S.A;Dept. of Statistical Science Southern Methodist University Dallas Texas 75275 U.S.A
Abstract:For a balanced two-way mixed model, the maximum likelihood (ML) and restricted ML (REML) estimators of the variance components were obtained and compared under the non-negativity requirements of the variance components by L ee and K apadia (1984). In this note, for a mixed (random blocks) incomplete block model, explicit forms for the REML estimators of variance components are obtained. They are always non-negative and have smaller mean squared error (MSE) than the analysis of variance (AOV) estimators. The asymptotic sampling variances of the maximum likelihood (ML) estimators and the REML estimators are compared and the balanced incomplete block design (BIBD) is considered as a special case. The ML estimators are shown to have smaller asymptotic variances than the REML estimators, but a numerical result in the randomized complete block design (RCBD) demonstrated that the performances of the REML and ML estimators are not much different in the MSE sense.
Keywords:Variance components  asymptotic variances  mean squared error  A  O  V  estimators  BIBD
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