University of Bahrain
Scientific Journals

Extended Mean with Distribution-Free Variance

Show simple item record Kandil, A. M. Hamza1, T. 2018-08-01T05:41:53Z 2018-08-01T05:41:53Z 2015
dc.identifier.issn 2384-4663
dc.description.abstract Location estimation is one of the basic activities in statistical data analysis so considerable effort has been put into the development of procedures for the robust estimation of measures of location. Because the distribution-free variance of most of existing measures is difficult to obtain in closed form, these measures work under strong modelling assumptions. We propose a robust location measure in which the expectation of a lower order statistics is replaced by the expectation of a larger order statistics. The main attraction of this measure is that its distribution-free variance is obtained in closed form. Comparisons with some of the best location estimators, mean, Hodges-Lehmann estimator, Huber's M-estimator and median are given based on Monte Carlo simulations. Computationally, the new estimator has an explicit expression and requires no iteration. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Huber's M-estimators
dc.subject L-statistics
dc.subject mean
dc.subject median
dc.subject order Statistics
dc.title Extended Mean with Distribution-Free Variance en_US
dc.type Article en_US
dc.volume 02
dc.issue 01
dc.source.title International Journal of Business and Statistical Analysis
dc.abbreviatedsourcetitle IJBSA

Files in this item

This item appears in the following Issue(s)

Show simple item record

Attribution-NonCommercial-ShareAlike 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International

All Journals

Advanced Search


Administrator Account