VARIANCE ESTIMATION IN SIMPLE RANDOM SAMPLING USING AUXILIARY INFORMATION

Year 2008, Volume: 37 Issue: 1, 57 - 67, 01.01.2008

Sat Gupta Javid Shabbir

Abstract

In this paper we focus on investigating the precision of several variance estimators considered by different authors, such as those suggested by Isaki (Variance estimation using auxiliary information, J. Amer. Stat. Assoc. 78, 117–123, 1983), and Kadilar and Cingi (Ratio estimators for population variance in simple and stratified sampling, Appl. Math. Comp. 173, 1047–1058 and Improvement in variance estimation using auxiliary information, Hacet. J. Math. Stat. 35 (1), 111–115, 2006). We propose a new hybrid class of estimators and show that in some cases their efficiency is better than the aforementioned traditional ratio and regression estimators of Isaki, and Kadilar and Cingi. Four numerical examples are considered to further evaluate the performance of these estimators.

Keywords

Auxiliary variable, Bias, Mean square error, Efficiency

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