Linear sufficiency with respect to a given vector of parametric functions
type: Item
instance of: scholarly article
title: Linear sufficiency with respect to a given vector of parametric functions
zbMATH Open document ID: 0614.62079
DOI: 10.1016/0378-3758(86)90171-0
author: Jerzy K. Baksalary
author: Radosław Kala
published in: Journal of Statistical Planning and Inference
publication date: 1986-01-01T00:00:00Z
review text: Let \(\{\) Y,X\(\beta\),V\(\}\) be a general Gauss-Markov model. FY is said to be linearly sufficient for the estimable function \(K\beta\) if the minimum dispersion linear unbiased estimator of \(K\beta\) is a linear function of FY. Theorem 1 gives a necessary and sufficient condition for such a linear sufficiency. Corollaries deal with some special cases, while theorem 2 considers the problem of linear minimal sufficiency. Applications are given to models with additional information on nuisance parameters, stepwise estimation and seemingly unrelated regressions.
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62B05
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 3994797
zbMATH Keywords: MDLUE
zbMATH Keywords: general Gauss-Markov model
zbMATH Keywords: linearly sufficient
zbMATH Keywords: minimum dispersion linear unbiased estimator
zbMATH Keywords: linear minimal sufficiency
zbMATH Keywords: nuisance parameters
zbMATH Keywords: stepwise estimation
zbMATH Keywords: seemingly unrelated regressions
MaRDI profile type: Publication
full work available at URL: 0378-3758(86)90171-0
OpenAlex ID: W2084864725
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description: scientific article; zbMATH DE number 3994797