On UMV-estimators in survey sampling
type: Item
instance of: scholarly article
title: On UMV-estimators in survey sampling
zbMATH Open document ID: 0277.62008
DOI: 10.1007/BF01893822
published in: Metrika
publication date: 1973-01-01T00:00:00Z
full work available at URL: https://eudml.org/doc/175606
review text: When a sample has been drawn from a finite population in order to estimate the population total of a certain characteristic, there are many unbiased estimators to chose among and even restriction to- linear unbiased estimators does usually not single out a unique estimator. In such a case it seems natural to apply some optimality criterion, e.g. minimization of the variance. A famous theorem of Godambe states that the class of linear unbiased estimators does in general not contain an estimator with uniformly minimal variance; here `in general' means `provided the sampling design is not a unicluster design'. In the present paper various previously published proofs of this theorem are critically examined and a new proof is offered. Further some comments are given on the concept of necessary bestness, due to Prabhu-Ajgaonkar.
Mathematics Subject Classification ID: 62D05
zbMATH DE Number: 3436441
author: Jan Lanke
MaRDI profile type: Publication
cites work: Q3227961
cites work: Q5725139
cites work: A Review of the Contributions Towards a Unified Theory of Sampling from Finite Populations
cites work: Hyper-Admissibility and Optimum Estimators for Sampling Finite Populations
cites work: Admissible Estimates for any Sampling Design
cites work: An Optimum Property of the Horvitz-Thomson Estimate
cites work: On the axioms of sample formation and their bearing on the construction of linear estimators in sampling theory for finite inverses
cites work: Q3250109
cites work: Q5582777
cites work: Q5594930
description: scientific article; zbMATH DE number 3436441