Robust HEWMA-type estimators for population mean under non-normality

被引：0
作者：
Jakhar A. [1 ]
Kumar S. [1 ]
机构：
[1] Department of Statistics, Central University of Rajasthan, Kishangarh, Rajasthan, Ajmer
来源：
Life Cycle Reliability and Safety Engineering | 2024年 / 13卷 / 1期
关键词：
05B05; 62K05; HEWMA; Memory-type estimator; Modified maximum likelihood; Non-normal; Simulation study; Time-based survey;
D O I：
10.1007/s41872-024-00244-y
中图分类号：
学科分类号：
摘要：
In the field of data science, the performance of a least square estimator is highly affected if the variable under study departs from normality or has the presence of outliers. This study proposes robust HEWMA-type estimators for the population mean by considering some auxiliary information under a non-normality assumption for a time-based survey. The proposed estimators are found to have the minimum mean square errors and biases compared to other relevant estimators for the long-tailed symmetric family. We have investigated the robustness properties of the proposed estimator in presence of the outliers. Finally, the authors provide a simulation study and a real-life application to support the theoretical outcomes of the proposed estimators over the relevant estimators. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
引用
收藏
页码：33 / 49
页数：16
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