Open AccessKINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS
Author(s) -
Hany Devita,
I Komang Gde Sukarsa,
I Putu Eka Nila Kencana
Publication year - 2014
Publication title -
e-jurnal matematika
Language(s) - English
Resource type - Journals
ISSN - 2303-1751
DOI - 10.24843/mtk.2014.v03.i04.p077
Subject(s) - multicollinearity , jackknife resampling , statistics , estimator , variance inflation factor , ordinary least squares , mathematics , ridge , regression , regression analysis , linear regression , mean squared error , simple linear regression , collinearity , econometrics , geography , cartography
Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached. Multicollinearity is a linear correlation between independent variabels in model. Jackknife Ridge Regression(JRR) as an extension of Generalized Ridge Regression (GRR) for solving multicollinearity. Generalized Ridge Regression is used to overcome the bias of estimators caused of presents multicollinearity by adding different bias parameter for each independent variabel in least square equation after transforming the data into an orthoghonal form. Beside that, JRR can reduce the bias of the ridge estimator. The result showed that JRR model out performs GRR model.