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基于鲁棒 M 估计的比率型估计量的改进回归。

Improved regression in ratio type estimators based on robust M-estimation.

机构信息

Division of Statistics and Computer Science, Chatha Jammu, India.

Department of Statistics, Dokuz Eylül University, Buca İzmir, Turkey.

出版信息

PLoS One. 2022 Dec 12;17(12):e0278868. doi: 10.1371/journal.pone.0278868. eCollection 2022.

Abstract

In this article, a new robust ratio type estimator using the Uk's redescending M-estimator is proposed for the estimation of the finite population mean in the simple random sampling (SRS) when there are outliers in the dataset. The mean square error (MSE) equation of the proposed estimator is obtained using the first order of approximation and it has been compared with the traditional ratio-type estimators in the literature, robust regression estimators, and other existing redescending M-estimators. A real-life data and simulation study are used to justify the efficiency of the proposed estimators. It has been shown that the proposed estimator is more efficient than other estimators in the literature on both simulation and real data studies.

摘要

本文提出了一种新的稳健比型估计量,使用英国的降序 M 估计量,用于在数据集存在异常值时,对简单随机抽样(SRS)中的有限总体均值进行估计。使用一阶逼近得到了该估计量的均方误差(MSE)方程,并与文献中的传统比型估计量、稳健回归估计量和其他现有的降序 M 估计量进行了比较。实际数据和模拟研究用于证明所提出的估计量的有效性。结果表明,在所提出的估计量在模拟和实际数据研究中都比文献中的其他估计量更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7c/9744333/c7bc4b2ad10b/pone.0278868.g001.jpg

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