Abstract
Copula models have been widely used to model the dependence between continuous random variables, but modelling count data via copulas has recently become popular in the statistics literature. Spearman's rho is an appropriate and effective tool to measure the degree of dependence between two random variables. In this paper, we derive the population version of Spearman's rho via copulas when both random variables are discrete. The closed-form expressions of the Spearman correlation are obtained for some copulas with different marginal distributions. We derive the upper and lower bounds of Spearman's rho for Bernoulli marginals. The proposed Spearman's rho correlations are compared with their corresponding Kendall's tau values and their functional relationships are characterized in some special cases. An extensive simulation study is conducted to demonstrate the validity of our theoretical results. Finally, we propose a bivariate copula regression model to analyse the count data of a cervical cancer dataset.
| Original language | English |
|---|---|
| Pages (from-to) | 1329-1355 |
| Number of pages | 27 |
| Journal | Statistics |
| Volume | 54 |
| Issue number | 6 |
| DOIs | |
| State | Published - 2020 |
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Keywords
- Spearman's rho
- bivariate measure of association
- concordance discordance dependence
- copula
