DSpace
 

KNUSTSpace >
Research Articles >
College of Science >

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/11373

Title: Leverages, Outliers and the Performance of Robust Regression Estimators
Authors: Adedia, David
Adebanji, Atinuke
Okyere, Eric
Agyen, James Kwaku
Keywords: Ordinary least squares estimator
Huber maximum likelihood estimator
least trimmed squares estimator
S-estimator
Modi ed maximum likelihood estimator
Power of the test
Leverages
Outliers
Issue Date: 2016
Publisher: British Journal of Mathematics & Computer Science
Citation: British Journal of Mathematics & Computer Science 15(3): 1-14, 2016, Article no.BJMCS.24281
Abstract: This study compared the performance of some robust regression methods and the Ordinary Least Squares Estimator (OLSE). The estimators were compared using varied levels of leverages and vertical outliers in the predictors and the dependent variables. An anthropometric dataset on total body fat with height, Body Mass Index (BMI), Triceps Skin-fold(TS), and arm fat as percent composition of the body (parmfat), as the predictors. The e ects of outliers and leverages on the estimators, were investigated at (5% leverages and 10% vertical outliers, 5% leverages with 15% vertical outliers). The criteria for the comparison: coe cients, Root Mean Square Error (RMSE), Relative E ciencies (RE), coe cients of determination (R-squared) and power of the test. The ndings from this study revealed that, OLSE was a ected by both outliers and leverages whilst Huber Maximum likelihood Estimator (HME) was a ected by leverages. The Least Trimmed Squares Estimator (LTSE) was slightly a ected by high perturbations of outliers and leverages. The study also showed that Modi ed Maximum likelihood Estimator (MME) and S Estimator (SE) were robust to all levels of outliers and leverage perturbations. Therefore leverages and outliers in datasets do a ect the post hoc power analysis of the methods which cannot resist them.
Description: An article published by British Journal of Mathematics & Computer Science, 15(3): 1-14, 2016, Article no.BJMCS.24281
URI: http://hdl.handle.net/123456789/11373
Appears in Collections:College of Science

Files in This Item:

File Description SizeFormat
Adedia&Adebanji1532016BJMCS24281.pdf536.76 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! DSpace Software Copyright © 2002-2010  Duraspace - Feedback