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PERBANDINGAN OPTIMALISASI MODEL REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN BIC (Studi Kasus: Model Regresi Ridge untuk Produk Domestik Regional Bruto (PDRB) Provinsi Jawa Tengah)
Linear regression modelshave some assumptions.One of them is the absence of multicollinearity. If there is a multicollinearity problem, there are several procedures that can be used to overcome the multicollinearity problem. Ridge regression models are one of models could be applied for a multicollinear dataset. In this research, selecting the best model for ridge regression will be applied using Bayesian Information Criterion (BIC) and Schall's Algorithm. Schall’s algorithm begins with choosing any initial penalty weight to estimate the ridge regression coefficient.While BIC is calculated from some penalty weights and the best model has the smallest BIC. The results of this study obtained that the Schall’s algorithm is more effective for optimizing the model than BIC. MSE and MAPE of the Schall’s algorithm smaller than MSE and MAPE of the BIC.
FAKULTAS SIANS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
PERBANDINGAN OPTIMALISASI MODEL REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN BIC (Studi Kasus: Model Regresi Ridge untuk Produk Domestik Regional Bruto (PDRB) Provinsi Jawa Tengah) (Text)
PERBANDINGAN OPTIMALISASI MODEL REGRESI RIDGE MENGGUNAKAN METODE ALGORITMA SCHALL DAN BIC (Studi Kasus: Model Regresi Ridge untuk Produk Domestik Regional Bruto (PDRB) Provinsi Jawa Tengah) (Other)
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Matematika