A new family of robust quantile-regression-based mean estimators using Sarndal approach

被引：3

作者：

Anas, Malik Muhammad ^{[1
]}

Huang, Zhengsheng ^{[1
]}

Shahzad, Usman ^{[2
,3
]}

Iftikhar, Soofia ^{[4
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Math & Stat, Nanjing, Jiangsu, Peoples R China

[2] Int Islamic Univ, Dept Math & Stat, Islamabad, Pakistan

[3] PMAS Arid Agr Univ, Dept Math & Stat, Rawalpindi, Pakistan

[4] Shaheed Benazir Bhutto Women Univ Peshawar, Dept Stat, Peshawar, KP, Pakistan

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2024年

关键词：

Quantile regression; Robust regression; Ratio-type estimators; Regression-type estimators; Simple random sampling; RATIO ESTIMATORS;

D O I：

10.1080/03610918.2024.2359504

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In presence of extreme-values or outliers, the ratio-type mean estimators are prominently developed by using the robust coefficients of regression and covariance matrices. In this article, we consider quantile regression coefficients while estimating the finite population mean. Incorporating Sarndal concept, we offer quantile-regression-based mean estimators under a simple random sample design. We calculate the mean squared error (MSE) for the suggested estimators and discover that they perform better than the existing estimators. Moreover, numerical representations and a simulation study support our discoveries in theory.

引用

页数：20

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