Outlier detection based on robust parameter estimates

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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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.

Item Type: Article
Uncontrolled Keywords: Outliers, least trimmed squares (LTS) and robust regression
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Depositing User: Fatin Safura
Date Deposited: 06 Mar 2022 03:27
Last Modified: 06 Mar 2022 03:27
URI: http://eprints.unisza.edu.my/id/eprint/5995

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