ON THE USE OF LOGISTIC REGRESSION AND LINEAR DISCRIMINANT ANALYSIS IN CLASSIFICATION PROBLEMS (A COMPARATIVE STUDY)

AUTHOR: AFUECHETA, EMMANUEL OSITA

DEPARTMENT: STATISTICS

AFFILIATION: NNAMDI AZIKIWE UNIVERSITY, AWKA

 In this study, we considered the problem of choosing between two statistical techniques, namely logistic regression and linear discriminant analysis which are mostly used for the analysis of categorical outcome variables. While both are appropriate for the development of linear classification models, linear discriminant analysis makes more assumptions about the underlying data. Hence, it is assumed that logistic regression is a more flexible and more robust method in case of violation of these assumptions. In this work, the comparison of the two methods is based on the normality assumption, different sample sizes, and number of variables. The performance of the two techniques is assessed using several measures of predictive accuracy, which are: B-index, Q-index, Cindex, and Classification error (CE). The results obtained show that logistic regression model agrees with linear discriminant analysis to an extent, but logistic regression slightly outperformed linear discriminant analysis especially when data are not normally distributed.

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