Advances and Applications in Statistics © 2018 Pushpa Publishing House, Allahabad, India http://www.pphmj.com http://dx.doi.org/10.17654/AS052060375 Volume 52, Number 6, 2018, Pages 375-389 ISSN: 0972-3617 Received: January 22, 2018; Accepted: March 25, 2018 Keywords and phrases: seemingly unrelated regression (SUR), weight least absolute deviations (WLAD), general weight least absolute deviations (GWLAD), weight least absolute deviations ridge (WLAD_Ridge), general weight least absolute deviations ridge (GWLAD_Ridge), least absolute deviations (LAD) cross validation criteria. WEIGHT LAD AND WEIGHT LAD RIDGE ESTIMATOR FOR SEEMINGLY UNRELATED REGRESSION MODELS Tarek M. Omara Department of Statistics, Mathematics and Insurance Faculty of Commerce Kafrelsheikh University Egypt Abstract In this paper, we introduce the four new estimators for seemingly unrelated regression (SUR) model, viz., weight least absolute deviations (WLAD), general weight least absolute deviations (WGLAD), weight least absolute deviations ridge (WLAD_Ridge) and general weight least absolute deviations ridge (GWLAD_Ridge) estimator. The LAD and GLAD estimators are sensitive to the leverage point, so the WLAD and WGLAD are suitable alternatives to deal with this problem. On the other hand, the ridge estimator is used when the predictors are highly collinear. The weight least absolute deviations ridge (WLAD_Ridge) and general weight least absolute deviations ridge (GWLAD_Ridge) estimators combine the interesting features of weight least absolute deviations (WLAD) and ridge estimators. The aim of these estimators is to resist the outliers, leverage point and at the same time shrinking coefficient to solve the multicollinearity simultaneously in SUR model. We chose ridge