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Generalized Class of Mean estimators with Known Measures for Outliers Treatment

Computer Systems Science & Engineering 2021年第7期38卷 1-15页

作者： Ibrahim M.Almanjahie Amer Ibrahim Al-Omari Emmanuel J.Ekpenyong Mir Subzar Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Statistical Research and Studies Support Unit King Khalid UniversityAbha62529Saudi Arabia Department of Mathematics Faculty of ScienceAl al-Bayt UniversityMafraq25113Jordan Department of Statistics Michael Okpara University of AgricultureUmudikeAbiaNigeria Jammu and Kashmir Institute of Mathematical Sciences Srinagar190008India

In estimation theory,the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares(OLS)method or robust regression techniques for estimating regression *** when the correlation is negative and the outliers are presented,the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased ***,this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation ***,we have proposed generalized estimators by using the ancillary information of non-conventional measures of dispersion(Gini’s mean difference,Downton’s method and probabilityweighted moment)using ordinary least squares and then finally adopting the Huber M-estimation technique on the suggested *** proposed estimators are investigated in the presence of outliers in both situations of negative and positive correlation between study and auxiliary *** comparisons and real data application are provided to show the strength of the proposed generalized *** is found that the proposed generalized Huber-M-type estimators are more efficient than the suggested generalized estimators under the OLS estimation method considered in this *** new proposed estimators will be useful in the future for data analysis and making decisions.

关键词： Product estimators ratio estimators regression estimators ordinary least square Huber M mean squared error efficiency

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