Linear sufficiency with respect to a given vector of parametric functions

Solutions are given to the problems concerned with characterizing transformations of a general Gauss-Markov model [ Y, Xβ, V ] into [ FY, FXβ, FVF′ ] such that the corresponding loss of information, if any, is irrelevant from the standpoint of determining the minimum dispersion linear unbiased estim...

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Bibliographic Details
Published in:Journal of statistical planning and inference 1986, Vol.14 (2), p.331-338
Main Authors: Baksalary, Jerzy K., Kala, Radosław
Format: Article
Language:English
Subjects:
Gauss-Markov model
Linearly minimal sufficient statistic
Linearly sufficient statistic
Minimum dispersion linear unbiased estimator
Nuisance parameters
Seemingly unrelated regressions
Stepwise estimation
Superfluous observations
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Summary:Solutions are given to the problems concerned with characterizing transformations of a general Gauss-Markov model [ Y, Xβ, V ] into [ FY, FXβ, FVF′ ] such that the corresponding loss of information, if any, is irrelevant from the standpoint of determining the minimum dispersion linear unbiased estimator of a given vector of estimable parametric functions.
ISSN:0378-3758
1873-1171
DOI:10.1016/0378-3758(86)90171-0