@phdthesis{digilib46920,
           month = {August},
           title = {PERBANDINGAN METODE ALGORITMA SCHALL DAN BAYESIAN INFORMATION CRITERION (BIC) DALAM PEMILIHAN MODEL TERBAIK REGRESI RIDGE},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM. 17106010018 ANISA NASWA LISTIANI},
            year = {2021},
            note = {Sri Utami Zuliana, S.Si., M.Sc., Ph.D},
        keywords = {Multiple Regressions, Multicolinearity, Ridge Regressions, Schall?s
Algorithm, BIC, MSE, MAPE},
             url = {https://digilib.uin-suka.ac.id/id/eprint/46920/},
        abstract = {Multiple regression analysis is a statistical method that analyzes more than
one independent variable againts a dependent variable. In the multiple
regression analysis there are some assumptions that must be fulfilled. One of
them is the absence of multicolinearity. Ridge regression is one of penalized
regression models that can overcame multicolinearity. Ridge regression
overcome multicolinearity by customize the least squares method of adds the
bias conjucture, on other hand, the number of residual squared tends to be
smaller than estimates gained by the least squares method, so a steady
coefficient is obtained. There are some selection methods of ridge regression. In
this research, Schall Algorithm and Bayesian Information Criterion (BIC) will
be compared for selecting the best model ridge regression trough looking at
MSE values and MAPE values. This research result is that the selection of the
model using Schall Algorithm is more effective than BIC in the dataset of infant
mortality in Central Java Province in 2019. The ridge regression model obtained
from Schall?s Algorithm is :
Y 0.4637PPM 0.0006KP 0.0005KN1 0.0000AE 0.0004PKB 0.0059BBLR}
}