Nusyirwan, Nusyirwan Selecting the Method to Overcome Partial and Full Multicollinearity in Binary Logistic Model. Jurusan Matematika, Universitas Lampung. (Unpublished)

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Abstract

The aim of our study is to select the best method for overcoming partial and full multicollinearity in binary logistic model for different sample sizes. Logistic ridge regression (LRR), least absolute shrinkage and selection operator (LASSO) and principal component logistic regression (PCLR) compared to maximum likelihood estimator (MLE) using simulation data with different level of multicollinearity and different sample sizes (n=20, 50, 100, 200). The best method is chosen based on mean square error (MSE) values and the best model is characterized by AIC value. The results show that LRR, LASSO and PCLR surpass MLE in overcoming partial and full multicollinearity in binary logistic model. PCLR exceeds LRR and LASSO when full multicollinearity occurs in binary logistic model but LASSO and LRR are better used when partial multicollinearity exists in the model

Item Type:	Other
Subjects:	Q Science > QA Mathematics
Depositing User:	NUSYIRWAN
Date Deposited:	13 May 2022 07:07
Last Modified:	13 May 2022 07:07
URI:	http://repository.lppm.unila.ac.id/id/eprint/41628

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