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On the extension of Gauss-Markov theorem to complex multivariate linear models

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Summary

The purpose of this paper is to develop a theory of linear estimation under various 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. An extension of the Gauss-Markov theorem to the standard multivariate model was first made by the author in [13]. In this paper, further generalizations of the result to multiresponse designs where the standard technique is inapplicable are considered.

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Author notes

  1. J. N. Srivastava

    Present address: Colorado State University, Colorado

Authors and Affiliations

  1. University of Nebraska, Nebraska, USA

    J. N. Srivastava

Authors
  1. J. N. Srivastava
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Additional information

This research was wholly supported by U.S. Air Force under Grant No. AF 33 (615)-3231, monitored by the Aerospace Research Laboratories.

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Srivastava, J.N. On the extension of Gauss-Markov theorem to complex multivariate linear models. Ann Inst Stat Math 19, 417–437 (1967). https://doi.org/10.1007/BF02911695

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  • DOI: https://doi.org/10.1007/BF02911695

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