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Outlier detection based on robust parameter estimates

Outliers can influence the analysis of data in various different ways. The outliers can lead to model misspecification, incorrect analysis results and can make all estimation procedures meaningless. In regression analysis, ordinary least square estimation is most frequently used for estimation o...

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Main Authors: Nyi Nyi, Naing, Nor Azlida, Aleng, Norizan, Mohamed, Kasypi, Mokhtar
Format: Article
Language:English
Published: 2017
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.unisza.edu.my/5995/1/FH02-ICODE-18-13380.pdf
http://eprints.unisza.edu.my/5995/
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spelling my-unisza-ir.59952022-03-06T03:27:03Z http://eprints.unisza.edu.my/5995/ Outlier detection based on robust parameter estimates Nyi Nyi, Naing Nor Azlida, Aleng Norizan, Mohamed Kasypi, Mokhtar TA Engineering (General). Civil engineering (General) Outliers can influence the analysis of data in various different ways. The outliers can lead to model misspecification, incorrect analysis results and can make all estimation procedures meaningless. In regression analysis, ordinary least square estimation is most frequently used for estimation of the parameters in the model. Unfortunately, this estimator is sensitive to outliers. Thus, in this paper we proposed some statistics for detection of outliers based on robust estimation, namely least trimmed squares (LTS). A simulation study was performed to prove that the alternative approach gives a better results than OLS estimation to identify outliers. 2017-12 Article PeerReviewed text en http://eprints.unisza.edu.my/5995/1/FH02-ICODE-18-13380.pdf Nyi Nyi, Naing and Nor Azlida, Aleng and Norizan, Mohamed and Kasypi, Mokhtar (2017) Outlier detection based on robust parameter estimates. International Journal of Applied Engineering Research, 12 (23). pp. 13429-13434. ISSN 0973-4562
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Nyi Nyi, Naing
Nor Azlida, Aleng
Norizan, Mohamed
Kasypi, Mokhtar
Outlier detection based on robust parameter estimates
description Outliers can influence the analysis of data in various different ways. The outliers can lead to model misspecification, incorrect analysis results and can make all estimation procedures meaningless. In regression analysis, ordinary least square estimation is most frequently used for estimation of the parameters in the model. Unfortunately, this estimator is sensitive to outliers. Thus, in this paper we proposed some statistics for detection of outliers based on robust estimation, namely least trimmed squares (LTS). A simulation study was performed to prove that the alternative approach gives a better results than OLS estimation to identify outliers.
format Article
author Nyi Nyi, Naing
Nor Azlida, Aleng
Norizan, Mohamed
Kasypi, Mokhtar
author_facet Nyi Nyi, Naing
Nor Azlida, Aleng
Norizan, Mohamed
Kasypi, Mokhtar
author_sort Nyi Nyi, Naing
title Outlier detection based on robust parameter estimates
title_short Outlier detection based on robust parameter estimates
title_full Outlier detection based on robust parameter estimates
title_fullStr Outlier detection based on robust parameter estimates
title_full_unstemmed Outlier detection based on robust parameter estimates
title_sort outlier detection based on robust parameter estimates
publishDate 2017
url http://eprints.unisza.edu.my/5995/1/FH02-ICODE-18-13380.pdf
http://eprints.unisza.edu.my/5995/
_version_ 1726796978407342080
score 13.250246

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