Accession Number : AD0647775

Title :   ON THE EXTENSION OF GAUSS-MARKOV THEOREM TO COMPLEX MULTIVARIATE LINEAR MODELS,

Corporate Author : NEBRASKA UNIV LINCOLN

Personal Author(s) : Srivastava,J. N.

Report Date : SEP 1966

Pagination or Media Count : 46

Abstract : The purpose of the paper is to develop a (distribution-free) theory of linear estimation under various complex multivariate linear models, which are more general than the usual model to which the standard techniques of multivariate analysis of variance are applicable. In particular, necessary and sufficient conditions under which (unique) best linear unbiased estimates of linear functions of (location) parameters exist are obtained. The extension of the Gauss-Markov Theorem to the standard multivariate model was first made by the author in a previous work. In this paper, the further generalizations of these results to multiresponse designs where the standard model is inapplicable are considered. (Author)

Descriptors :   (*THEOREMS, *MULTIVARIATE ANALYSIS), MODEL THEORY, EXPERIMENTAL DESIGN, COMBINATORIAL ANALYSIS, ANALYSIS OF VARIANCE

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE