Version 1
: Received: 23 July 2018 / Approved: 23 July 2018 / Online: 23 July 2018 (10:58:36 CEST)
How to cite:
Giannouli, P.; Kountzakis, C.E. The Use of PCA in Reduction of Credit Scoring Modeling Variables: Evidence from Greek Banking System. Preprints2018, 2018070412. https://doi.org/10.20944/preprints201807.0412.v1.
Giannouli, P.; Kountzakis, C.E. The Use of PCA in Reduction of Credit Scoring Modeling Variables: Evidence from Greek Banking System. Preprints 2018, 2018070412. https://doi.org/10.20944/preprints201807.0412.v1.
Cite as:
Giannouli, P.; Kountzakis, C.E. The Use of PCA in Reduction of Credit Scoring Modeling Variables: Evidence from Greek Banking System. Preprints2018, 2018070412. https://doi.org/10.20944/preprints201807.0412.v1.
Giannouli, P.; Kountzakis, C.E. The Use of PCA in Reduction of Credit Scoring Modeling Variables: Evidence from Greek Banking System. Preprints 2018, 2018070412. https://doi.org/10.20944/preprints201807.0412.v1.
Abstract
In this paper, we use the Principal Components Logistic Regression as a technique to reduce the variables being used in Credit Scoring Modeling. Specifically, we construct two models in which greek enterprises are classified, through their credit behavior and we evaluate them, relying on real data. In general, we propose a general way to use PC Regression, in case that we have high correlations and categorical variables in the sample.
Keywords
P.C. regression; AIC criterion; logit function; pearson's Chi-square use
Subject
SOCIAL SCIENCES, Econometrics & Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
