Abstract: Ordinary least
square is parameter estimation method for linier regression analysis by
minimizing residual sum of square. In the presence of multicollinearity,
estimators which are unbiased and have a minimum variance can not be generated.
Multicollinearity refers to a situation where regressor variables are highly
correlated. Generalized Ridge Regression is an alternative method to deal with
multicollinearity problem. In Generalized Ridge Regression, different biasing
parameters for each regressor variables were added to the least square equation
after transform the data to the space of orthogonal regressors. The analysis
showed that Generalized Ridge Regression was satisfactory to overcome
multicollinearity.
Keywords: Linear regression;
parameter estimation; multicollinearity; Generalized Ridge Regression
Penulis: NI KETUT TRI UTAMI, I
KOMANG GDE SUKARSA
Kode Jurnal: jpmatematikadd130016
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