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COMPARISON OF VARIANCE ESTIMATORS FOR THE RATIO ESTIMATOR BASED ON SMALL SAMPLE
作者姓名:秦怀振  李莉莉
作者单位:QIN HUAIZHEN (Department of Mathematics,Beijing Normal University,Beijing 100875,China) LI LILI (Institute of System Sciences,Academy of Mathematics and System Sciences,Chinese Academg of Sciences,Beijing 100080,China)
基金项目:the National Natural Science Foundation of China (No.10071091)
摘    要:1. Introduction and Main ResultsSuppose the population of interest consists of N distinct units labelled by 1,' f N.Associated with unit i are two values K and Xi, with Xi > 0 (i = 1,' t N). Denote thepopulation means of K and X, by Y and X respectively. To estimate Y, it is customaryto select a simple raPdom sample of size n and to use the ratio estimatNn = RX if Xis available, where R = y/x is an estimator for population ratio R = Y/X, y and x arerespectively the 8ample mean8 of…

收稿时间:30 May 1999

Comparison of variance estimators for the ratio estimator based on small sample
Qin Huaizhen,Li Lili.COMPARISON OF VARIANCE ESTIMATORS FOR THE RATIO ESTIMATOR BASED ON SMALL SAMPLE[J].Acta Mathematicae Applicatae Sinica,2001,17(4):449-456.
Authors:Qin Huaizhen  Li Lili
Institution:(1) Department of Mathematics, Beijing Normal University, 100875 Beijing, China;(2) Institute of System Sciences, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, 100080 Beijing, China
Abstract:This paper sheds light on all open problem put forward by Cochran1]. The comparison between two commonly used variance estimators v1(^R) and v2(^R) of the ratio estimator R for population ratio R from small sample selected by simple random sampling is made following the idea of the estimated loss approach (See 2]). Considering the superpopulation model under which the ratio estimator ^-YR for population mean -Y is the best linear unbiased one, the necessary and sufficient conditions for v1(^R) v2(^R) and v2(^R) v1(^R) are obtained with ignored the sampling fraction f. For a substantial f, several rigorous sufficient conditions for v2(^R) v1(^R) are derived.
Keywords:Superpopulation model  expected mean square error  simple random sampling  negligible sampling fraction  substantial sampling fraction
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