Title of article :
Robust Estimator to Deal with Regression Models Having both Continuous and Categorical Regressors: A Simulation Study
Author/Authors :
Talib, Bashar A. Universiti Putra Malaysia - Faculty of Science - Department of Mathematics, Malaysia , Midi, Habshah Universiti Putra Malaysia - Institute for Mathematical Research (INSPEM) - Laboratory of Applied and Computational Statistics, Malaysia , Midi, Habshah Universiti Putra Malaysia - Faculty of Science - Department of Mathematics, Malaysia
Abstract :
The Ordinary Least Squares (OLS) method has been the most popular technique for estimating the parameters of the multiple linear regression. However, in the presence of outliers and when the model includes both continuous and categorical (factor) variables, the OLS can result in poor estimates. In this paper we try to introduce an alternative robust method for such a model that is much less influenced by the presence of outliers.
Keywords :
Outliers , Leverage points , Robust Distance , S , M , estimates , RLSRDL1 , RLSRDSM
Journal title :
Malaysian Journal of Mathematical Sciences
Journal title :
Malaysian Journal of Mathematical Sciences
