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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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 |
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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/ |
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1726796978407342080 |
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13.19449 |