REGRESI RIDGE DENGAN BENTUK Q DAN FAKTOR SHRINKAGE MENGGUNAKAN SINGULAR VALUE DECOMPOSITION

• Main Authors: , RIZKI ARISTA SP, , Drs. Zulaela,Dipl.Med.Stats.,M.Si
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2014
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/131263/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=71715

One of the assumptions in a multiple linear regression is no multicollinearity or no linear relationship between the independent variables. Multicollinearity causes the MSE of the least squares estimators become so great and the estimator obtained from the least squares method is not appropriate. In fact, the study is expected in a model that has minimum variance, though biased. Ridge regression with Q-shape and shrinkage factors is one way to solve the problem of multicollinearity because it produces small MSE despite the relatively small bias. This method reduced the coefficient of linear regression using shrinkage factors to shrink the coefifisien and controlling them using the Q-shape. This method is a modification of the ordinary ridge regression method using singular value decomposition by change the component of independent variable without changing the characteristics of the independent variables.