A Maximal Extension of the Gauss-Markov Theorem and Its Nonlinear Version

Takeaki Kariya and Hiroshi Kurata

Journal of Multivariate Analysis, 2002, vol. 83, issue 1, 37-55

Abstract: In this paper, first we make a maximal extension of the well-known Gauss-Markov Theorem (GMT) in its linear framework. In particular, the maximal class of distributions of error term for which the GMT holds is derived. Second, we establish a nonlinear version of the maximal GMT and describe some interesting families of distributions of error term for which the nonlinear GMT holds.

Keywords: Gauss-Markov; theorem; nonlinear; versions; of; Gauss-Markov; theorem; location-equivariant; estimator; generalized; least; squares; estimator; elliptically; symmetric; distribution (search for similar items in EconPapers)
Date: 2002
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