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International Journal of Mathematical, Engineering and Management Sciences

ISSN: 2455-7749 . Open Access


A Comparative Study of Multivariate Analysis Techniques for Highly Correlated Variable Identification and Management

A Comparative Study of Multivariate Analysis Techniques for Highly Correlated Variable Identification and Management

K. Ntotsis
Lab of Statistics and Data Analysis, Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Greece.

E. N. Kalligeris
Lab of Statistics and Data Analysis, Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Greece.

A. Karagrigoriou
Lab of Statistics and Data Analysis, Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Greece.

DOI https://doi.org/10.33889/IJMEMS.2020.5.1.004

Received on August 12, 2019
  ;
Accepted on October 01, 2019

Abstract

In this work we attempt is to locate and analyze via multivariate analysis techniques, highly correlated covariates (factors) which are interrelated with the Gross Domestic Product and therefore are affecting either on short-term or on long-term its shaping. For the analysis, feature selection techniques and model selection criteria are used. The case study focuses on annual data for Greece for the period 1980-2018.

Keywords- Multicollinearity, Correlation feature selection, Model selection criteria, Multivariate analysis, Principal component analysis.

Citation

Ntotsis, K., Kalligeris, E. N., & Karagrigoriou, A. (2020). A Comparative Study of Multivariate Analysis Techniques for Highly Correlated Variable Identification and Management. International Journal of Mathematical, Engineering and Management Sciences, 5(1), 45-55. https://doi.org/10.33889/IJMEMS.2020.5.1.004.