On one multivariate linear model with nuisance parameters
Pavla Kunderová (1997)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Pavla Kunderová (1997)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Pavla Kunderová (2004)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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The multivariate linear model, in which the matrix of the first order parameters is divided into two matrices: to the matrix of the useful parameters and to the matrix of the nuisance parameters, is considered. We examine eliminating transformations which eliminate the nuisance parameters without loss of information on the useful parameters and on the variance components.
Pavla Kunderová (1998)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Lubomír Kubáček (2010)
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
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Multivariate models frequently used in many branches of science have relatively large number of different structures. Sometimes the regularity condition which enable us to solve statistical problems are not satisfied and it is reasonable to recognize it in advance. In the paper the model without constraints on parameters is analyzed only, since the greatness of the class of such problems in general is out of the size of the paper.
Lubomír Kubáček (2013)
Applications of Mathematics
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The cross-covariance matrix of observation vectors in two linear statistical models need not be zero matrix. In such a case the problem is to find explicit expressions for the best linear unbiased estimators of both model parameters and estimators of variance components in the simplest structure of the covariance matrix. Univariate and multivariate forms of linear models are dealt with.