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Robust ridge regression approach for combined multicollinearity-outlier problem

Link to publisher's homepage at https://amci.unimap.edu.my/

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Main Authors: Sanizah, Ahmad, Aliah Natasha, Affindi
Other Authors: saniz924@uitm.edu.my
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2023
Subjects:
Multicollinearity
Outliers
Ridge regression
Laplace
Cauchy distribution
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77723
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spelling my.unimap-777232023-01-25T04:30:43Z Robust ridge regression approach for combined multicollinearity-outlier problem Sanizah, Ahmad Aliah Natasha, Affindi saniz924@uitm.edu.my Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam Multicollinearity Outliers Ridge regression Laplace Cauchy distribution Link to publisher's homepage at https://amci.unimap.edu.my/ Ordinary least squares (OLS) offers good parameter estimates in regression if all assumptions are met. However, if the assumptions are not adhered to due to the presence of combined multicollinearity and outliers, parameter estimates may be severely distorted. Hence, robust parameter estimates were injected into the ridge regression method to produce robust ridge regression models. Therefore, the aim of this study is to investigate the performance of selected robust ridge estimators which include S, M, MM and Least Trimmed Squares (LTS) estimators via a simulation study. Laplace and Cauchy error distributions were introduced as outliers in the simulated data of various sample sizes and levels of multicollinearity. The performance of the estimation methods is investigated using criteria bias and root mean square error (RMSE). The finding indicates that Ridge LTS is the best robust ridge estimator in handling data containing both multicollinearity and outliers due to its smallest value in the RMSE. Applications of the estimators to two benchmark real-life datasets provide similar results. 2023-01-25T04:30:43Z 2023-01-25T04:30:43Z 2022-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 123-132 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77723 en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Multicollinearity
Outliers
Ridge regression
Laplace
Cauchy distribution
spellingShingle Multicollinearity
Outliers
Ridge regression
Laplace
Cauchy distribution
Sanizah, Ahmad
Aliah Natasha, Affindi
Robust ridge regression approach for combined multicollinearity-outlier problem
description Link to publisher's homepage at https://amci.unimap.edu.my/
author2 saniz924@uitm.edu.my
author_facet saniz924@uitm.edu.my
Sanizah, Ahmad
Aliah Natasha, Affindi
format Article
author Sanizah, Ahmad
Aliah Natasha, Affindi
author_sort Sanizah, Ahmad
title Robust ridge regression approach for combined multicollinearity-outlier problem
title_short Robust ridge regression approach for combined multicollinearity-outlier problem
title_full Robust ridge regression approach for combined multicollinearity-outlier problem
title_fullStr Robust ridge regression approach for combined multicollinearity-outlier problem
title_full_unstemmed Robust ridge regression approach for combined multicollinearity-outlier problem
title_sort robust ridge regression approach for combined multicollinearity-outlier problem
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2023
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77723
_version_ 1772813101239894016
score 13.214268

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