Modeling and Classification Multicollinear Variables using Multinomial Ridge Logistic Regression Aprroach - Giatma Dwijuna Ahadi; Ismaini Zain; Santi Puteri Rahayu (Indonesian Journal of Applied Statistics, 2025)
Multinomial Logistic Regression is a method used to find relationships between nominal or multinomial response variables (Y) with one or more predictor variables. Logistics Regression is a classic method that is often used to solve classification problems. Assumptions on Logistics Regression are models containing multicollinearity. Ridge Logistic Estimator (RLE) is methods to solve multicollinearity cases in Logistic Regression. Wu & Asar proposed a new ridge value that can also reduce bias in parameter estimation. Therefore, this research will discuss about Multinomial Ridge Logistic and selection the best of ridge constant values. The performance test of the ridge value will be applied to the Iris Dataset in R software. The best criteria for improvement ridge constant value by looking at the smallest standard error. The calculation results show that the Wu-Asar approach is the best ridge constant and Wald individual test shows significant results. Based on the result, show that the Wu-Asar Ridge constant value on Multinomial Ridge Logistic Regression are very good performance in estimated smaller standar error. The classification for dataset shows high results with 98% global accuracy.Keywords: multinomial; ridge logistic regression; Wu-Asar; standard error; classification