APLIKASI GENERALIZED RIDGE REGRESSION UNTUK MENGATASI MASALAH MULTIKOLINEARITAS - , VALENDRA GRANITHA SHANDIKA P. ([Yogyakarta] : Universitas Gadjah Mada, 2014)
Least square method is one of parameter estimation method, it is a method for estimating regression coefficient. If any of classical regression assumption, that is no multicollinearity is not met, parameter estimation using least square method becomes less valid, even if there is perfect multicollinearity then it causes beta parameters canât be estimated. Linear relationship between the independent variables causes the variance parameter beta becomes large, the error was large. In fact, the estimated value we desired is a small variance and error. For handling this multicollinearity problem, one of the way is using ridge regression. The concept of ridge regression is by adding a k biased constant which is a diagonal matrix, to the correlation matrix ' X X . In this paper we will discuss one of the k parameter estimation methods, namely Generalized Ridge Regression. This method obtain the k ridge parameter that is not a single parameter but multiple k ridge parameters. Those k ridge parameters are different for each independent variables.