文章基本信息

标题：Almost unbiased optimum estimators for population mean using dual auxiliary information
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作者：Muhammad Irfan ; Maria Javed ; Sajjad Haider Bhatti 等
期刊名称：Journal of King Saud University - Science
印刷版ISSN：1018-3647
出版年度：2020
卷号：32
期号：6
页码：2835-2844
DOI：10.1016/j.jksus.2020.07.006
语种：English
出版社：Elsevier
摘要：AbstractOne eminent disadvantage of many existing optimal estimators/class of estimators is that they are typically biased. In this article, we proposed an optimum class of unbiased estimators for estimating the population mean under simple random sampling without replacement (SRSWOR) scheme. Proposed class is a blend of three concepts: 1) information on auxiliary variable, 2) the ranks of auxiliary variable and 3) Hartley-Ross type unbiased estimation procedure. Expressions for the bias and the minimum variance of the new class are derived up to first degree of approximation. To highlight the application of proposed class, five real data sets are used. Numerical findings confirm that the new class behaves efficiently as compared to traditional unbiased estimator and other almost unbiased estimators under study. In addition, Monte Carlo simulation study is conducted through two real populations to assess the performance of proposed class against competitors. On the basis of theoretical and numerical findings, it is concluded that new proposed class can generate optimum unbiased estimators under SRSWOR scheme. Therefore, use of proposed class is recommended for future applications.
关键词：Auxiliary variable;Hartley-Ross type estimator;Ranked auxiliary variable;Unbiased;Variance