On estimation of variance components with constraints

The so-called linear approach to estimating linear functions of variance and/or covariance components clearly shows the well-known connection between quadratic, invariant and unbiased, estimation and the classical linear theory methods, LSE and BLUE. This approach allows us to consider and to use fu...

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Bibliographic Details
Published in:Journal of statistical planning and inference 1998-06, Vol.69 (1), p.81-87
Main Author: WITKOVSKY, V
Format: Article
Language:English
Subjects:
62F30
62J10
Bayes quadratic estimation
Elliptical restrictions
Exact sciences and technology
Full and partial restrictions
LBQE(U, I)
Linear inference, regression
Mathematics
Minimax invariant quadratic estimation
MINQE(U, I)
MIVQE(U, I)
Nonparametric inference
Probability and statistics
Sciences and techniques of general use
Statistics
Variance and covariance components
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Summary:The so-called linear approach to estimating linear functions of variance and/or covariance components clearly shows the well-known connection between quadratic, invariant and unbiased, estimation and the classical linear theory methods, LSE and BLUE. This approach allows us to consider and to use further results of the linear theory for variance-covariance components estimation. In the present paper the minimax invariant quadratic estimators of linear functions of variance-covariance components under full or partial restrictions on the parameter set are investigated.
ISSN:0378-3758
1873-1171
DOI:10.1016/S0378-3758(97)00127-4