An optimality criterion for vector unbiased statistical estimation functions
type: Item
instance of: scholarly article
title: An optimality criterion for vector unbiased statistical estimation functions
zbMATH Open document ID: 0664.62018
DOI: 10.1016/0378-3758(88)90021-3
author: B. Chandrasekar
published in: Journal of Statistical Planning and Inference
publication date: 1988-01-01T00:00:00Z
review text: The author and \textit{B. K. Kale} [ibid. 9, 45-54 (1984; Zbl 0535.62030)] considered the problem of estimating a vector interesting parameter in the presence of nuisance parameters through vector unbiased statistical estimation functions (USEFs) and obtained an extension of the Cramér- Rao inequality. Based on this result, three optimality criteria were proposed and their equivalence was established. In this paper, motivated by the uniformly minimum risk criterion [see \textit{S. Zacks}, The theory of statistical inference. (1971), p. 102] for estimators, we propose a new optimality criterion for vector USEFs in the nuisance parameter case and show that it is equivalent to the three existing criteria.
Mathematics Subject Classification ID: 62F10
Mathematics Subject Classification ID: 62A01
zbMATH DE Number: 4086749
zbMATH Keywords: vector interesting parameter
zbMATH Keywords: nuisance parameters
zbMATH Keywords: vector unbiased statistical estimation functions
zbMATH Keywords: extension of the Cramér-Rao inequality
zbMATH Keywords: optimality criteria
zbMATH Keywords: equivalence
zbMATH Keywords: uniformly minimum risk criterion
MaRDI profile type: Publication
full work available at URL: 0378-3758(88)90021-3
OpenAlex ID: W2075650870
cites work: Unbiased statistical estimation functions for parameters in presence of nuisance parameters
cites work: Multiparametric estimating equations
cites work: Conditional likelihood and unconditional optimum estimating equations
cites work: Estimating equations in the presence of a nuisance parameter
cites work: Q5736619
description: scientific article; zbMATH DE number 4086749