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Estimation and Forecasting in Models of Binary Logistic Regression with Dependent Regressors

Student: Yulia Feofanova

Supervisor: Elena R. Goryainova

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2017

The paper provides a classification method which is based on the logistic regression and modified principal component analysis procedure. Having conducted an experiment using simulated data, we have shown that the proposed method improves classification in terms of accuracy and ROC-curve analysis compared to basic logistic regression, support vector machines, decision trees and k nearest neighbors. Performance check using real dataset has also been carried and shown that proposed algorithm both improves classification quality and significantly reduces dimension of the data.

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