Some Improved Classes of Estimators in Stratified Sampling Using Bivariate Auxiliary Information

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-08-31 DOI:10.1155/2022/2660114

Shashi Bhushan, Anoop Kumar, Rodney Onyango, Saurabh Singh

引用次数: 4

Abstract

This manuscript considers some improved combined and separate classes of estimators of population mean using bivariate auxiliary information under stratified simple random sampling. The expressions of bias and mean square error of the proposed classes of estimators are determined to the first order of approximation. It is exhibited that under some particular conditions, the proposed classes of estimators dominate the existing prominent estimators. The theoretical findings are supported by a simulation study performed over a hypothetically generated population.

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基于二元辅助信息的分层抽样中的一些改进估计类

本文研究了分层简单随机抽样下使用二元辅助信息的几种改进的组合和分离类总体均值估计。这类估计器的偏置和均方误差的表达式被确定为一阶近似。证明了在某些特殊条件下，所提出的估计量类优于现有的突出估计量。这些理论发现得到了对假设产生的人口进行的模拟研究的支持。

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

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.