On some efficient classes of estimators using auxiliary attribute

Q4 Mathematics

Statistics in Transition Pub Date : 2023-03-15 DOI:10.59170/stattrans-2023-025

Shashi Bhushan, Anoop Kumar

引用次数: 1

Abstract

This paper considers some efficient classes of estimators for the estimation of population mean using known population proportion. The usual mean estimator, classical ratio, and regression estimators suggested by Naik and Gupta (1996) and Abd-Elfattah et al. (2010) estimators are identified as the members of the suggested class of estimators. The expressions of bias and mean square errors are derived up to first-order approximation. The proposed estimators were put to test against various other competing estimators till date. It has been found both theoretically and empirically that the suggested classes of estimators dominate the existing estimators.

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利用辅助属性的几种有效估计量

本文考虑了利用已知的总体比例估计总体均值的几种有效估计量。Naik和Gupta(1996)以及Abd-Elfattah等人(2010)提出的通常均值估计量、经典比率估计量和回归估计量被确定为建议的估计量类的成员。偏差和均方误差的表达式被导出到一阶近似。到目前为止，所提出的估计器已与其他各种竞争估计器进行了测试。从理论和经验两方面都发现，建议的估计量类别支配着现有的估计量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics in Transition Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

9 weeks

期刊介绍： Statistics in Transition (SiT) is an international journal published jointly by the Polish Statistical Association (PTS) and the Central Statistical Office of Poland (CSO/GUS), which sponsors this publication. Launched in 1993, it was issued twice a year until 2006; since then it appears - under a slightly changed title, Statistics in Transition new series - three times a year; and after 2013 as a regular quarterly journal." The journal provides a forum for exchange of ideas and experience amongst members of international community of statisticians, data producers and users, including researchers, teachers, policy makers and the general public. Its initially dominating focus on statistical issues pertinent to transition from centrally planned to a market-oriented economy has gradually been extended to embracing statistical problems related to development and modernization of the system of public (official) statistics, in general.