Outlier detection based on robust parameter estimates - Nyi Nyi, Naing; Nor Azlida, Aleng; Norizan, Mohamed; Kasypi, Mokhtar (2017)
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.