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

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)

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1966
Accession Number
AD0647775

Entities

People

  • J. N. Srivastava

Organizations

  • University of Nebraska–Lincoln

Tags

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Computing-Related Activities
  • Data Science
  • Information Science
  • Interdisciplinary Science
  • Mathematical Analysis
  • Mathematics
  • Multivariate Analysis
  • Standards

Fields of Study

  • Mathematics

Readers

  • Regression Analysis.
  • Statistical inference.