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Title: Test of linear hypothesis in multivariate models (English)
Author: Kubáček, Lubomír
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 43
Issue: 4
Year: 2007
Pages: 463-470
Summary lang: English
Category: math
Summary: In regular multivariate regression model a test of linear hypothesis is dependent on a structure and a knowledge of the covariance matrix. Several tests procedures are given for the cases that the covariance matrix is either totally unknown, or partially unknown (variance components), or totally known. (English)
Keyword: multivariate model
Keyword: linear hypothesis
Keyword: variance components
Keyword: insensitive region
MSC: 62H15
MSC: 62J05
idZBL: Zbl 1134.62034
idMR: MR2377924
Date available: 2009-09-24T20:25:36Z
Last updated: 2012-06-06
Stable URL:
Reference: [1] Anderson T. W.: Introduction to Multivariate Statistical Analysis.Wiley, New York 1958 Zbl 1039.62044, MR 0091588
Reference: [2] Kubáček L., Kubáčková, L., Volaufová J.: Statistical Models with Linear Structures.Veda (Publishing House of Slovak Academy of Sciences), Bratislava 1995
Reference: [3] Lešanská E.: Optimization of the size of nonsensitiveness regions.Appl. Math. 47 (2002), 9–23 MR 1876489
Reference: [4] Rao C. R.: Linear Statistical Inference and Its Applications.Second edition. Wiley, New York 1973 Zbl 0256.62002, MR 0346957
Reference: [5] Rao C. R., Kleffe J.: Estimation of Variance Components and Applications.North–Holland, Amsterdam 1988 Zbl 0645.62073, MR 0933559
Reference: [6] Rao C. R., Mitra S. K.: Generalized Inverse of Matrices and Its Applications.Wiley, New York 1971 Zbl 0261.62051, MR 0338013


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