International Journal of Nonlinear Analysis and Applications
Comparison of COVID-19 data analysis between classical dependencies and correlations via copulas
Document Type : Research Paper
Authors
Department of Mathematics, Faculty of Education, University of Kufa, Iraq
Abstract
In this paper, we analyze the covid-19 data set in two ways, The first one depends on the calculation of correlation coefficient via classical mathematical representation. And the second way of analysis depends on modern technique which is associated with copula function concepts and its relationship to measures of association. Afterwards, we compare the obtained results to decide far which is better in an analysis of the examined dataset.
Keywords
[1] A.A. Ahmed and O. Hassan, Comments on copula functions and their relationship to probability density functions,
Iraqi J. Sci. 2020 (2020) 1115–1122.
[2] S.I. Bangdiwala, Regression: simple linear, Int. J. Injury Cont. Safety Promotion 25(1) (2018) 113–115.[3] L. Benettazzo, Copula VAR Models With Applications to Genetic Networks, Universita degli Studi di Padova
Dipartimento di Scienze Statistiche Corso di Laurea Magistrale in Scienze Statistiche, 2017.
[4] P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin and S. Zeger, Springer Series in Statistics, Springer, New
York, 2008.
[5] N.S. Chok, Pearson’s Versus Spearman’s and Kendall’s Correlation Coefficients for Continuous Data, Doctoral
dissertation, University of Pittsburgh, (2016).
[6] J.C. De Winter, S.D. Gosling and J. Potter, Comparing the Pearson and Spearman correlation coefficients across
distributions and sample sizes: A tutorial using simulations and empirical data, Psych. Methods 21(3) (2016).
[7] Y. Elouerkhaoui, Credit correlation, Palgrave Macmillan, 2017.
[8] J.D. Gibbons and S. Chakraborti, Nonparametric Statistical Inference, Fourth Edition: Revised and Expanded
(Statistics: A Series of Textbooks and Monographs), CRC Press; 4th edition, 2003.
[9] S. Kautish, S.L. Peng and A.J. Obaid, Computational intelligence techniques for combating COVID-19, Springer
International Publishing, 2021.
[10] A.D. Lovie, Who discovered Spearman’s rank correlation?, Brit. J. Math. Statist. Psych. 48(2) (1995) 255–269
[11] R.B. Nelsen, An introduction to copulas, Springer Science & Business Media, 2007.
[12] A. Sklar, Fonctions de r´epartition `a n dimensions et leurs marges, Publications de l’Institut Statistique de
l’Universit´e de Paris. 8 (1959) 229–231.
[13] X. Zhang and H. Jiang, Application of copula function in financial risk analysis, Comput. Electric. Engin. 77
(2019) 376–388.
Iraqi J. Sci. 2020 (2020) 1115–1122.
[2] S.I. Bangdiwala, Regression: simple linear, Int. J. Injury Cont. Safety Promotion 25(1) (2018) 113–115.[3] L. Benettazzo, Copula VAR Models With Applications to Genetic Networks, Universita degli Studi di Padova
Dipartimento di Scienze Statistiche Corso di Laurea Magistrale in Scienze Statistiche, 2017.
[4] P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin and S. Zeger, Springer Series in Statistics, Springer, New
York, 2008.
[5] N.S. Chok, Pearson’s Versus Spearman’s and Kendall’s Correlation Coefficients for Continuous Data, Doctoral
dissertation, University of Pittsburgh, (2016).
[6] J.C. De Winter, S.D. Gosling and J. Potter, Comparing the Pearson and Spearman correlation coefficients across
distributions and sample sizes: A tutorial using simulations and empirical data, Psych. Methods 21(3) (2016).
[7] Y. Elouerkhaoui, Credit correlation, Palgrave Macmillan, 2017.
[8] J.D. Gibbons and S. Chakraborti, Nonparametric Statistical Inference, Fourth Edition: Revised and Expanded
(Statistics: A Series of Textbooks and Monographs), CRC Press; 4th edition, 2003.
[9] S. Kautish, S.L. Peng and A.J. Obaid, Computational intelligence techniques for combating COVID-19, Springer
International Publishing, 2021.
[10] A.D. Lovie, Who discovered Spearman’s rank correlation?, Brit. J. Math. Statist. Psych. 48(2) (1995) 255–269
[11] R.B. Nelsen, An introduction to copulas, Springer Science & Business Media, 2007.
[12] A. Sklar, Fonctions de r´epartition `a n dimensions et leurs marges, Publications de l’Institut Statistique de
l’Universit´e de Paris. 8 (1959) 229–231.
[13] X. Zhang and H. Jiang, Application of copula function in financial risk analysis, Comput. Electric. Engin. 77
(2019) 376–388.
)
Volume 12, Special Issue
December 2021Pages 2257-2264
- Receive Date: 07 October 2021
- Revise Date: 26 November 2021
- Accept Date: 02 December 2021