Applied Mathematical Sciences, Vol. 7, 2013, no. 50, 2469 - 2480 HIKARI Ltd, www.m-hikari.com Ridge Regression Estimators with the Problem of Multicollinearity Maie M. Kamel Statistic Department, Faculty of Commerce Tanta Univeristy, Tanta, Egypt maie.m.kamel@gmail.com Sarah F. Aboud Egyptian Academy of Computers, Information & Management Technology Ministry of High Education, Tanta, Egypt Sara_aboud@yahoo.com Copyright © 2013 Maie M. Kamel and Sarah F. Aboud. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract The study aims to illustrate the negative effect of the Multicollinearity problem upon the specimen, identify the way of Ridge Regression as a way to deal with the Multicollinearity problem, focus on some of the estimators of Ridge regression as (James and Stein, Bhattacharya, Heuristic) and identify which estimator from the previously mentioned estimators is highly preferable to be used, to estimate the parameters of a model which faces the Multicollinearity problem. Minimum mean-square error (MSE) has been used as the best measure for estimator. Application has been done on specific data for return on total assets of a bank after making sure that this data faces the Multicollinearity problem. Also, simulation method was used to generate fabricated data sets, which gave more space in the application. According to the study we can see that James and Stein’s estimator has got the minimum mean square error (MSE). Consequently the study recommends its usage to estimate model parameters which face the Multicollinearity problem.