NDLI: Unbiased and almost unbiased ratio estimators of the population mean in ranked set sampling

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Unbiased and almost unbiased ratio estimators of the population mean in ranked set sampling

Content Provider	Springer Nature Link
Author	Perron, François Majidi, Saeed Jafari Jozani, Mohammad
Copyright Year	2011
Abstract	In this paper we study the problem of reducing the bias of the ratio estimator of the population mean in a ranked set sampling (RSS) design. We first propose a jackknifed RSS-ratio estimator and then introduce a class of almost unbiased RSS-ratio estimators of the population mean. We also present an unbiased RSS-ratio estimator of the mean using the idea of Hartley and Ross (Nature 174:270–271, 1954) which performs better than its counterpart with simple random sample data. We show that under certain conditions the proposed unbiased and almost unbiased RSS-ratio estimators perform better than the commonly used (biased) RSS-ratio estimator in estimating the population mean in terms of the mean square error. The theoretical results are augmented by a simulation study using a wheat yield data set from the Iranian Ministry of Agriculture to demonstrate the practical benefits of our proposed ratio-type estimators relative to the RSS-ratio estimator in reducing the bias in estimating the average wheat production.
Ending Page	737
Page Count	19
Starting Page	719
File Format	PDF
ISSN	09325026
e-ISSN	16139798
Journal	Statistical Papers
Issue Number	3
Volume Number	53
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-02-27
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ratio estimator Simple random sampling Almost unbiased Statistics for Business/Economics/Mathematical Finance/Insurance Jackknife Operations Research/Decision Theory Economic Theory Probability Theory and Stochastic Processes Ranked set sampling Auxiliary variable Sampling theory, sample surveys Relative efficiency
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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