University of Guilan Journal of Mathematical Modeling 2345-394X 13 4 2025 12 01 Dimension reduction by identifying and removing redundant variables using copula function 803 815 8837 10.22124/jmm.2025.28169.2484 EN Kianoush Fathi Vajargah Department of Statistics, Islamic Azad University, North Tehran Branch, Tehran, Iran Hamid Mottaghi Golshan Department of Mathematics, Islamic Azad University, Shahriar Branch, Shahriar, Iran Fazel Badakhshan Department of Statistics, Islamic Azad University, North Tehran Branch, Tehran, Iran Journal Article 2024 08 16 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. https://jmm.guilan.ac.ir/article_8837_0c888c51a714133e97ce97d6e8ad0f58.pdf