Journal ArticleDOI
Estimating the parameters of mixed linear models with modal estimators
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In this paper, a procedure to estimate the variance components and fixed effects of mixed linear models is presented, where the mode of the joint posterior distribution of all the parameters is obtained by an iterative technique.Abstract:
This paper presents a procedure to estimate the variance components and fixed effects of mixed linear models. The mode of the joint posterior distribution of all the parameters is obtained by an iterative technique. The proposed method is illustrated with one-way and two-fold nested random models. Two numerical examples demonstrate the iterative solution.read more
Citations
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A bibliography on variance components an introduction and an update: 1984-2002
Hardeo Sahai,Anwer Khurshid +1 more
TL;DR: In particular, the study of variance through a class of linear models known as random and mixed models is a central topic in statistics with wide ramifications in both theory and applications as discussed by the authors.
Journal ArticleDOI
Estimating the parameters of mixture models with modal estimators
TL;DR: In this article, the authors extended some of the work presented in Redner and Walker [I9841] on the maximum likelihood estimate of parameters in a mixture model to a Bayesian modal estimate.
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Variance components estimation for the balanced random effects model under mixed prior distributions
R. Ahmad,S.M. Mostafa +1 more
TL;DR: For the balanced random effects models, when the variance components are correlated either naturally or through common prior structures, the authors proposed some new Bayesian estimators, which have smaller mean squared errors than the MVUE and RMLE.
References
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Book
Bayesian inference in statistical analysis
George E. P. Box,George C. Tiao +1 more
TL;DR: In this article, the effect of non-normality on inference about a population mean with generalizations was investigated. But the authors focused on the effect on the mean with information from more than one source.
Journal ArticleDOI
Bayesian Inference in Statistical Analysis
Journal ArticleDOI
Maximum-likelihood estimation for the mixed analysis of variance model
H. O. Hartley,J. N. K. Rao +1 more
TL;DR: A procedure is developed for the maximum-likelihood estimation of the unknown constants and variances included in the general mixed analysis of variance model, involving fixed and random factors and interactions, and applies to all cases where the design matrices satisfy certain conditions.
Journal ArticleDOI
Estimation in Covariance Components Models
TL;DR: In this article, the estimation of fixed and random effects when the variances and covariances are known is presented in Bayesian terms, point estimates of the unknown variances are computed using the EM algorithm for maximum likelihood estimation from incomplete data.