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Title: Multivariate models with constraints confidence regions (English)
Author: Kubáček, Lubomír
Language: English
Journal: Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
ISSN: 0231-9721
Volume: 47
Issue: 1
Year: 2008
Pages: 83-100
Summary lang: English
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Category: math
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Summary: In multivariate linear statistical models with normally distributed observation matrix a structure of a covariance matrix plays an important role when confidence regions must be determined. In the paper it is assumed that the covariance matrix is a linear combination of known symmetric and positive semidefinite matrices and unknown parameters (variance components) which are unbiasedly estimable. Then insensitivity regions are found for them which enables us to decide whether plug-in approach can be used for confidence regions. (English)
Keyword: multivariate model
Keyword: constraints
Keyword: variance components
Keyword: plug-in estimator
Keyword: insensitivity region
MSC: 62F30
MSC: 62H12
MSC: 62J05
idZBL: Zbl 1165.62043
idMR: MR2482719
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Date available: 2009-08-27T11:26:13Z
Last updated: 2012-05-04
Stable URL: http://hdl.handle.net/10338.dmlcz/133406
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Reference: [1] Anderson T. W.: Introduction to Multivariate Statistical Analysis. : J. Wiley, New York., 1958. MR 0091588
Reference: [2] Fišerová E., Kubáček L., Kunderová P.: Linear Statistical Models: Regularity, Singularities. : Academia, Praha., 2007.
Reference: [3] Kshirsagar A. M.: Multivariate Analysis. : M. Dekker, New York., 1972. MR 0343478
Reference: [4] Kubáček L., Kubáčková L., Volaufová J.: Statistical Models with Linear Structures. : Veda (Publishing House of Slovak Academy of Sciences), Bratislava., 1995.
Reference: [5] Rao C. R.: Linear Statistical Inference, Its Applications. : J. Wiley, New York–London–Sydney., 1965. MR 0221616
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