Dimension reduction by identifying and removing redundant variables using copula function

Document Type : Research Article

Authors

1 Department of Statistics, Islamic Azad University, North Tehran Branch, Tehran, Iran

2 Department of Mathematics, Islamic Azad University, Shahriar Branch, Shahriar, Iran

Abstract

In today's world, rapid developments in science and engineering are increasingly adding up to larger amounts of data; as a result, numerous problems have emerged in the analysis of big data. Hence, data dimensionality reduction can accelerate data analysis and even yield better results without losing any useful data.  A copula represents an appropriate model of dependence to compare multivariate distributions and better detect the relationships of data. Therefore, a copula is employed in this study to identify and delete noisy data  from the original data.  Then, it is compared to  the principal component analysis to show its superiority.

Keywords

Journal of Mathematical Modeling
Volume 13, Issue 4
December 2025
Pages 803-815

Files

Share

How to cite

Statistics