On The Tractability of Some Discordancy Statistics for Modelling Outliers in a Univariate Time Series (Published)
This paper compares the tractability of four discordancy statistics for modelling outliers based on extremeness. They are: the Generlaized Extreme Studentized Deviate (ESD), Grubb’s test, Hampel’s method and the quartile method. The last two methods are seen to detect outliers even for datasets that are not approximately normal, although Hampel outperforms the quartile method in some cases. However, a multiplier effect of 2.2 is proposed for the quartile method in addition to the robust statistics for accommodating the outliers.
Keywords: Autocorrelation function (ACF)., Generalized Extreme Studentized Deviate (ESD), Outliers, Time Series
Alternative Outliers Detection Procedures In Linear Regression Analysis : A Comparative Study (Published)
A Common problem in linear regression analysis is outliers, which produces undesirable effects on the least squares estimates. Many widely used regression diagnostics procedures have been introduced to detect these outliers. However, such diagnostics, which are based on the least squares estimates, are not efficient and cannot detect correctly swamping and masking effects. In this paper, we attempt to investigate the robustness of some well known diagnostics tools, namely, Cook’s distance, the Welsch-Kuh distance and the Hadi measure. The robust version of these diagnostics based on the Huber-M estimation have been proposed to identify the outliers. A simulation study is performed to compare the performance of the classical diagnostics with the proposed versions. The findings of this study indicate that, the proposed alternative versions seem to be reasonable well and should be considered as worthy robust alternative to the least squares method
Keywords: Diagnostics, Huber–M Estimation, Least Squares Method, Linear Regression, Outliers, Simulation Study
