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Handling multicollinearity and outliers using weighted ridge least trimmed squares

Common problems in multiple linear regression models are multicollinearity and outliers. In this paper, we will propose a robust ridge regression. It is based on weighted ridge least trimmed squares (WRLTS). The proposed method (WRLTS) has been compared to some different estimation methods, namely t...

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Main Authors: Pati, Kafi Dano, Adnan, Robiah, Saffari, Seyed Ehsan, Rasheed, Bello Abdulkadir
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/61111/1/RobiahAdnan2014_HandlingMulticollinearityandOutliers.pdf
http://eprints.utm.my/id/eprint/61111/
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spelling my.utm.611112017-03-15T00:19:34Z http://eprints.utm.my/id/eprint/61111/ Handling multicollinearity and outliers using weighted ridge least trimmed squares Pati, Kafi Dano Adnan, Robiah Saffari, Seyed Ehsan Rasheed, Bello Abdulkadir QA Mathematics Common problems in multiple linear regression models are multicollinearity and outliers. In this paper, we will propose a robust ridge regression. It is based on weighted ridge least trimmed squares (WRLTS). The proposed method (WRLTS) has been compared to some different estimation methods, namely the Ordinary Least Squares (OLS), Ridge Regression (RR),Robust Ridge Regression (RRR) such as Ridge LeastMedian Squares (RLMS), Ridge Least Trimmed Squares (RLTS) regression based on LTS estimator and Weighted Ridge (WRID) with respect to Standard Error. Two examples are used to illustrate the proposed method. In both examples, WRLTS is found to be the best estimator among the other methods in this paper. 2014 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/61111/1/RobiahAdnan2014_HandlingMulticollinearityandOutliers.pdf Pati, Kafi Dano and Adnan, Robiah and Saffari, Seyed Ehsan and Rasheed, Bello Abdulkadir (2014) Handling multicollinearity and outliers using weighted ridge least trimmed squares. In: Second International Science Postgraduate Conference, 10-12 Mac, 2014, Skudai, Johor.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Pati, Kafi Dano
Adnan, Robiah
Saffari, Seyed Ehsan
Rasheed, Bello Abdulkadir
Handling multicollinearity and outliers using weighted ridge least trimmed squares
description Common problems in multiple linear regression models are multicollinearity and outliers. In this paper, we will propose a robust ridge regression. It is based on weighted ridge least trimmed squares (WRLTS). The proposed method (WRLTS) has been compared to some different estimation methods, namely the Ordinary Least Squares (OLS), Ridge Regression (RR),Robust Ridge Regression (RRR) such as Ridge LeastMedian Squares (RLMS), Ridge Least Trimmed Squares (RLTS) regression based on LTS estimator and Weighted Ridge (WRID) with respect to Standard Error. Two examples are used to illustrate the proposed method. In both examples, WRLTS is found to be the best estimator among the other methods in this paper.
format Conference or Workshop Item
author Pati, Kafi Dano
Adnan, Robiah
Saffari, Seyed Ehsan
Rasheed, Bello Abdulkadir
author_facet Pati, Kafi Dano
Adnan, Robiah
Saffari, Seyed Ehsan
Rasheed, Bello Abdulkadir
author_sort Pati, Kafi Dano
title Handling multicollinearity and outliers using weighted ridge least trimmed squares
title_short Handling multicollinearity and outliers using weighted ridge least trimmed squares
title_full Handling multicollinearity and outliers using weighted ridge least trimmed squares
title_fullStr Handling multicollinearity and outliers using weighted ridge least trimmed squares
title_full_unstemmed Handling multicollinearity and outliers using weighted ridge least trimmed squares
title_sort handling multicollinearity and outliers using weighted ridge least trimmed squares
publishDate 2014
url http://eprints.utm.my/id/eprint/61111/1/RobiahAdnan2014_HandlingMulticollinearityandOutliers.pdf
http://eprints.utm.my/id/eprint/61111/
_version_ 1643655073475067904
score 13.211869

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