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Restricted BLUP for Mixed Linear Models
Author(s) -
McGilchrist C. A.,
Aisbett C. W.
Publication year - 1991
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710330202
Subject(s) - best linear unbiased prediction , mathematics , random effects model , statistics , restricted maximum likelihood , estimator , mixed model , generalized linear mixed model , joint probability distribution , random variate , linear regression , linear model , maximum likelihood , random variable , econometrics , computer science , selection (genetic algorithm) , medicine , meta analysis , artificial intelligence
A new estimation procedure for mixed regression models is introduced. It is a development of Henderson's best linear unbiased prediction procedure which uses the joint distribution of the observed dependent random variables and the unknown realisations of the random components of the model. It is proposed to replace the likelihood of the observations given the random components by the asymptotic likelihood of the maximum likelihood estimators and the prior distribution of the random components by a restricted prior distribution which is consistent with the usual restrictions placed on the random components when they are considered conditionally fixed.