Thamrin Tayeb


New stepwise method is a method of selecting predictor variables in a linear reg- ression model. This method is an extension of the principal component regressi- on, and consists of the selection of the original predictor variables iteratively at the same time, a group of main subset component is selected repeatedly. This me- thod has also the basic properties of the stepwise method. Thus we will get the best combination of stepwise selection and principal component selection me- thods. Model that is obtained by using this method characterizes a low-valued PRESS. The application of this method is not only for linear model, but also can  be expanded to generalized linear models. The comparison of both methods are based on the R2 criteria in the variable selection, obtained R2 value results which are almost the same as those models in the case of solid waste of data, so having payed fully attention to the number of predictor variables entered into the mo- dels, it can be said that the new stepwise method tends to be better than the prin- cipal component regression.


Multikolinearitas; pemilihan variabel; komponen utama


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DOI: https://doi.org/10.24252/lp.2012v15n2a3


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