Displaying similar documents to “Best linear plus quadratic unbiased estimation of parameters in mixed linear models”

On maximum likelihood estimation in mixed normal models with two variance components

Mariusz Grządziel (2014)

Discussiones Mathematicae Probability and Statistics

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In the paper we deal with the problem of parameter estimation in the linear normal mixed model with two variance components. We present solutions to the problem of finding the global maximizer of the likelihood function and to the problem of finding the global maximizer of the REML likelihood function in this model.

Modified minimax quadratic estimation of variance components

Viktor Witkovský (1998)

Kybernetika

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The paper deals with modified minimax quadratic estimation of variance and covariance components under full ellipsoidal restrictions. Based on the, so called, linear approach to estimation variance components, i. e. considering useful local transformation of the original model, we can directly adopt the results from the linear theory. Under normality assumption we can can derive the explicit form of the estimator which is formally find to be the Kuks–Olman type estimator.

Quotient of information matrices in comparison of linear experiments for quadratic estimation

Czesław Stępniak (2017)

Open Mathematics

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The ordering of normal linear experiments with respect to quadratic estimation, introduced by Stępniak in [Ann. Inst. Statist. Math. A 49 (1997), 569-584], is extended here to the experiments involving the nuisance parameters. Typical experiments of this kind are induced by allocations of treatments in the blocks. Our main tool, called quotient of information matrices, may be interesting itself. It is known that any orthogonal allocation of treatments in blocks is optimal with respect...