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A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model


Citation

Alguraibawi, Mohammed and Midi, Habshah and Rahmatullah Imon, A. H. M. (2015) A new robust diagnostic plot for classifying good and bad high leverage points in a multiple linear regression model. Mathematical Problems in Engineering, 2015. pp. 1-12. ISSN 1024-123X

Abstract / Synopsis

Identification of high leverage point is crucial because it is responsible for inaccurate prediction and invalid inferential statement as it has a larger impact on the computed values of various estimates. It is essential to classify the high leverage points into good and bad leverage points because only the bad leverage points have an undue effect on the parameter estimates. It is now evident that when a group of high leverage points is present in a data set, the existing robust diagnostic plot fails to classify them correctly. This problem is due to the masking and swamping effects. In this paper, we propose a new robust diagnostic plot to correctly classify the good and bad leverage points by reducing both masking and swamping effects. The formulation of the proposed plot is based on the Modified Generalized Studentized Residuals. We investigate the performance of our proposed method by employing a Monte Carlo simulation study and some well-known data sets. The results indicate that the proposed method is able to improve the rate of detection of bad leverage points and also to reduce swamping and masking effects.


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Additional Metadata

Item Type: Article
Divisions: Institute for Mathematical Research
DOI Number: https://doi.org/10.1155/2015/279472
Publisher: Hindawi Publishing Corporation
Keywords: Robust diagnostic; Multiple linear
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 29 Jun 2016 01:10
Last Modified: 29 Jun 2016 01:10
Altmetrics: httphttp://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1155/2015/279472
URI: http://psasir.upm.edu.my/id/eprint/43528
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