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Robust heteroscedasticity consistent covariance matrix estimator based on robust mahalanobis distance and diagnostic robust generalized potential weighting methods in linear regression


Citation

Midi, Habshah and Sani, Muhammad and Arasan, Jayanthi (2018) Robust heteroscedasticity consistent covariance matrix estimator based on robust mahalanobis distance and diagnostic robust generalized potential weighting methods in linear regression. Journal of Modern Applied Statistical Methods, 17 (1). art. no. eP2596. pp. 1-24. ISSN 1538-9472

Abstract

The violation of the assumption of homoscedasticity and the presence of high leverage points (HLPs) are common in the use of regression models. The weighted least squares can provide the solution to heteroscedastic regression model if the heteroscedastic error structures are known. Based on Furno (1996), two robust weighting methods are proposed based on HLP detection measures (robust Mahalanobis distance based on minimum volume ellipsoid and diagnostic robust generalized potential based on index set equality (DRGP(ISE)) on robust heteroscedasticity consistent covariance matrix estimators. Results obtained from a simulation study and real data sets indicated the DRGP(ISE) method is superior.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
DOI Number: https://doi.org/10.22237/jmasm/1530279855
Publisher: JMASM
Keywords: Linear regression; Robust HCCM estimator; Ordinary least squares; Weighted least squares; High leverage points
Depositing User: Ms. Nida Hidayati Ghazali
Date Deposited: 06 May 2020 17:57
Last Modified: 06 May 2020 17:57
Altmetrics: https://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.22237/jmasm/1530279855
URI: http://psasir.upm.edu.my/id/eprint/73815
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