Small area quantile estimation based on distribution function using linear mixed models

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2021-06-01 DOI:10.18559/ebr.2021.2.7

Tomasz Stachurski

引用次数: 0

Abstract

Abstract In economic studies researchers are often interested in the estimation of the distribution function or certain functions of the distribution function such as quantiles. This work focuses on the estimation quantiles as inverses of the estimates of the distribution function in the presence of auxiliary information that is correlated with the study variable. In the paper a plug-in estimator of the distribution function is proposed which is used to obtain quantiles in the population and in the small areas. Performance of the proposed method is compared with other estimators of the distribution function and quantiles using the simulation study. The obtained results show that the proposed method usually has smaller relative biases and relative RMSE comparing to other methods of obtaining quantiles based on inverting the distribution function.

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基于线性混合模型分布函数的小面积分位数估计

在经济研究中，研究人员经常对分布函数或分布函数的某些函数(如分位数)的估计感兴趣。这项工作的重点是在与研究变量相关的辅助信息存在的情况下，作为分布函数估计的逆的估计分位数。本文提出了一种分布函数的插入式估计器，用于获得总体和小区域的分位数。通过仿真研究，将该方法的性能与其他分布函数和分位数估计方法进行了比较。结果表明，与其他基于分布函数反求的分位数方法相比，该方法具有较小的相对偏差和相对RMSE。

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.