Title of article
A Maximal Extension of the Gauss–Markov Theorem and Its Nonlinear Version
Author/Authors
Kariya، نويسنده , , Takeaki and Kurata، نويسنده , , Hiroshi، نويسنده ,
Issue Information
دوفصلنامه با شماره پیاپی سال 2002
Pages
19
From page
37
To page
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
nonlinear versions of Gauss–Markov theorem , Gauss–Markov theorem , location-equivariant estimator , generalized least squares estimator , elliptically symmetric distribution
Journal title
Journal of Multivariate Analysis
Serial Year
2002
Journal title
Journal of Multivariate Analysis
Record number
1557816
Link To Document