Displaying similar documents to “Different kinds of sufficiency in the general Gauss-Markov model”

On eliminating transformations for nuisance parameters in multivariate linear model

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.

Multivariate statistical models; solvability of basic problems

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.

Stability of invariant linearly sufficient statistics in the general Gauss-Markov model

Andrzej Kornacki (1997)

Applications of Mathematics

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Necessary and sufficient conditions are derived for the inclusions J 0 ⊂ J and J 0 * ⊂ J * to be fulfilled where J 0 , J 0 * and J , J * are some classes of invariant linearly sufficient statistics (Oktaba, Kornacki, Wawrzosek (1988)) corresponding to the Gauss-Markov models G M 0 = ( y , X 0 β 0 , σ 0 2 V 0 ) and G M = ( y , X β , σ 2 V ) , respectively.