GROUP DIAGNOSTIC MEASURES OF DIFFERENT TYPES OF OUTLIERS IN MULTIPLE LINEAR REGRESSION MODEL
Article Sidebar
Main Article Content
Abstract
The topic of detection outliers is one of the crucial topics that have been of interest to researchers in many scientific fields. The presence of outliers in the dataset may lead to the breakdown of the estimator of the method in use. The statistical literature has shown that several types of outliers occur according to the type and nature of the data. Therefore, the researchers concentrated on identifying the type of outliers of statistical models by using two diagnostic procedures, individual and grouped. Unfortunately, the first procedure neglects the effect of the phenomenon of (masking and swamping). In contrast, the second procedure has not been able to eliminate this phenomenon ideally but rather reduce the rates of its appearance. This paper seeks to suggest improving one of the well-known group diagnostic methods (DRGP) by using an RMVN location and scale matrix instead of MVE to reduce the effect of (swamping). A newly proposed method denoted as DRGP(RMVN) is tested with a simulation study and real data. The results have shown that the performance of our proposed method is more efficient than (DRGP.MVE) to reduce the swamping points.
Downloads
Article Details
Transfer of Copyrights
- In the event of publication of the manuscript entitled [INSERT MANUSCRIPT TITLE AND REF NO.] in the Malaysian Journal of Science, I hereby transfer copyrights of the manuscript title, abstract and contents to the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) for the full legal term of copyright and any renewals thereof throughout the world in any format, and any media for communication.
Conditions of Publication
- I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures, maps, photographs and other visual materials of whom the copyrights is owned by a third party.
- This manuscript contains no statements that are contradictory to the relevant local and international laws or that infringes on the rights of others.
- I agree to indemnify the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) in the event of any claims that arise in regards to the above conditions and assume full liability on the published manuscript.
Reviewer’s Responsibilities
- Reviewers must treat the manuscripts received for reviewing process as confidential. It must not be shown or discussed with others without the authorization from the editor of MJS.
- Reviewers assigned must not have conflicts of interest with respect to the original work, the authors of the article or the research funding.
- Reviewers should judge or evaluate the manuscripts objective as possible. The feedback from the reviewers should be express clearly with supporting arguments.
- If the assigned reviewer considers themselves not able to complete the review of the manuscript, they must communicate with the editor, so that the manuscript could be sent to another suitable reviewer.
Copyright: Rights of the Author(s)
- Effective 2007, it will become the policy of the Malaysian Journal of Science (published by the Faculty of Science, University of Malaya) to obtain copyrights of all manuscripts published. This is to facilitate:
(a) Protection against copyright infringement of the manuscript through copyright breaches or piracy.
(b) Timely handling of reproduction requests from authorized third parties that are addressed directly to the Faculty of Science, University of Malaya. - As the author, you may publish the fore-mentioned manuscript, whole or any part thereof, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
You may produce copies of your manuscript, whole or any part thereof, for teaching purposes or to be provided, on individual basis, to fellow researchers. - You may include the fore-mentioned manuscript, whole or any part thereof, electronically on a secure network at your affiliated institution, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- You may include the fore-mentioned manuscript, whole or any part thereof, on the World Wide Web, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- In the event that your manuscript, whole or any part thereof, has been requested to be reproduced, for any purpose or in any form approved by the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers), you will be informed. It is requested that any changes to your contact details (especially e-mail addresses) are made known.
Copyright: Role and responsibility of the Author(s)
- In the event of the manuscript to be published in the Malaysian Journal of Science contains materials copyrighted to others prior, it is the responsibility of current author(s) to obtain written permission from the copyright owner or owners.
- This written permission should be submitted with the proof-copy of the manuscript to be published in the Malaysian Journal of Science
References
A.H.M.R, Imon, Identifying multiple high leverage points in linear regression. Journal of Statistical Studies, Special Volume in Honour of Professor Mir Masoom Ali. 3(2002), 207–218.
Devlin, Susan J, Gnanadesikan, Ramanathan, & Kettenring, Jon R, Robust estimation of dispersion matrices and principal components, J J. AM. STAT. ASSOC., 76 (1981), 354-362.
F. R.Hampel, E. M. Ronchetti, P. Rousseeuw and W. A. Stahel, Robust Statistics,Wiley, New York,(1986).
H.Midi, N. Ramli, A.H.M.RImon, The performance of Diagnostic-Robust Generalized Potentials to identify multiple high leverage points in linear regression, J. APPL STAT. 36(2009): 507-520.
H. S. Uraibi, H. Midi, On Robust Bivariate and Multivariate Correlation Coefficient, Economic Computation & EconomicCybernetics Studies & Research, 53(2019), 2.
H. S. Uraibi, S. A. Alhussieny, Improvise Group Diagnostic Potential Measure for Multivariate Normal Data, Al-Qadisiyah Journal for Administrative and Economic Sciences, 23,(2021),2.
I-Cheng Yeh, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998).
Olive, David J., A resistant estimator of multivariate location and dispersion, Computational statistics & data analysis,,46,(2004), 93-102.
Olive, David J, & Hawkins, Douglas M., Robust multivariate location and dispersion. Preprint, (2010) (www. math.siu. Edu/olive/preprints. htm).
P.J. Huber, Robust Statistics, Wiley, New York, (1981).
P.j. Rousseeuw and A. M. Leroy, Robust Regression and Outlier Detection, Wiley, New York, (1987).
P. j. Rousseeuw and B. Van Zomeren, Unmasking multivariate outliers and leverage points, J. Am. STAT. ASSOC., 85(1990), 633-639
P. J., Rousseeuw, Least median of squares regression, J. AM. STAT. ASSOC., 79(1984), 871–880.
R.A. Maronna, R. D. Martin and V.J. Yohai, Robust Statistics Theory and Methods. New York: Willy and sons, (2006).