A comparison of some robust regression techniques

Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Industrial Engineering, Turkey

Approval Date: 2009

Student: EZGİ AVCI



Robust regression is a commonly required approach in industrial studies like data mining, quality control and improvement, and finance areas. Among the robust regression methods; Least Median Squares, Least Trimmed Squares, Mregression, MM-method, Least Absolute Deviations, Locally Weighted Scatter Plot Smoothing and Multivariate Adaptive Regression Splines are compared under contaminated normal distributions with each other and Ordinary Least Squares with respect to the multiple outlier detection performance measures. In this comparison; a simulation study is performed by changing some of the parameters such as outlier density, outlier locations in the x-axis, sample size and number of independent variables. In the comparison of the methods, multiple outlier detection is carried out with respect to the performance measures detection capability, false alarm rate and improved mean square error and ratio of improved mean square error. As a result of this simulation study, the three most competitive methods are compared on an industrial data set with respect to the coefficient of multiple determination and mean square error.