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Selective overview of forward selection in terms of robust correlations


Uraibi, Hassan Sami and Midi, Habshah and Rana, Sohel (2017) Selective overview of forward selection in terms of robust correlations. Communications in Statistics - Simulation and Computation, 46 (7). pp. 5479-5503. ISSN 0361-0918; ESSN: 1532-4141


Forward selection (FS) is a very effective variable selection procedure for selecting a parsimonious subset of covariates from a large number of candidate covariates. Detecting the type of outlying observations, such as vertical outliers or leverage points, and the FS procedure are inseparable problems. For robust variable selection, a crucial issue is whether the outliers are univariate, bivariate, or multivariate. This paper uses a consistent robust multivariate dispersion estimator to obtain robust correlation estimators used to establish robust forward selection (RFS) procedures that outperform methods that use robust bivariate correlations. The usefulness of our proposed procedure is studied with a numerical example and a simulation study. The result shows the proposed method has scalability and the ability to deal with univariate, bivariate and multivariate outlying observations including leverage points or vertical outliers, and the new method outperforms previously published methods of RFS.

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Additional Metadata

Item Type: Article
Divisions: Institute for Mathematical Research
Faculty of Science
DOI Number: https://doi.org/10.1080/03610918.2016.1164862
Publisher: Taylor & Francis
Keywords: Adjusted winsorization; Forward selection; RFCH; Robust correlation; Robust variable selection
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 16 Aug 2018 01:32
Last Modified: 28 Sep 2018 03:30
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/03610918.2016.1164862
URI: http://psasir.upm.edu.my/id/eprint/63189
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