Some new measures of dependence for random variables based on Spearman's ρ and Kendall's τ

Dawei Lu, Lingyue Zhang, Xiaoguang Wang and Lixin Song

Journal of Nonparametric Statistics, 2018, vol. 30, issue 4, 860-883

Abstract: In this paper, we extend the traditional Spearman's ρ and Kendall's τ which are widely used to measure the dependence between continuous random variables to the generalised ones that can measure the dependence between discrete or even more general random variables. Furthermore, applying these two generalised correlation coefficients to the trinomial distribution, we study how they vary with the parameter, and point out they are more reasonable than Pearson's correlation coefficient in some ways. Based on Spearman's ρ and Kendall's τ, two new measures are proposed with their respective asymptotic distributions. Finally, we run a Monte Carlo experiment and give the example analysis to investigate the performance of our dependence measures.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2018.1486403 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:30:y:2018:i:4:p:860-883

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2018.1486403

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().