M-Estimation Use Bisquare, Hampel, Huber, and Welsch Weight Functions in Robust Regression

Authors(3) :-Tiyas Yulita, Khairil Anwar Notodiputro, Kusman Sadik

The estimation by the least squares method (LSM) is often used in simple or multiple regression model. However, it was not uncommon for the response variables  in model  which contain contamination or outliers. LSM is known will be very sensitive to these problem, so if LSM is still used in regression then parameter estimate can be bias. Robust regression is well known as a method that robust from effect of outliers in order to obtain better result from LSM. The paper will discuss the methods of M-estimation to model the response data which contain the outliers using Bisquare, Hampel, Huber, and Welsch weight function using simulation data and HDI (Human Development Index) data in West Java Province. On the HDI data, the M estimations prediction method with the Welsch weight function yields , the best of other weight functions.

Authors and Affiliations

Tiyas Yulita
Department of Statistics, Faculty of Science and Technology, Nahdlatul Ulama Lampung University, Indonesia
Khairil Anwar Notodiputro
Department of Statistics, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Indonesia
Kusman Sadik
Department of Statistics, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Indonesia

M-Estimator, Robust Regression, Weight Function

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Publication Details

Published in : Volume 4 | Issue 9 | July-August 2018
Date of Publication : 2018-08-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 425-430
Manuscript Number : IJSRSET184964
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Tiyas Yulita, Khairil Anwar Notodiputro, Kusman Sadik, " M-Estimation Use Bisquare, Hampel, Huber, and Welsch Weight Functions in Robust Regression, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 9, pp.425-430, July-August-2018.
Journal URL : http://ijsrset.com/IJSRSET184964

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