Goodness-of-fit tests for copulas
Journal of Multivariate Analysis
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Journal of Multivariate Analysis - Special issue dedicated to Professor Yasunori Fujikoshi
Estimation of interclass correlation via a Kotz-type distribution
Computational Statistics & Data Analysis
A goodness of fit test for copulas based on Rosenblatt's transformation
Computational Statistics & Data Analysis
Multivariate conditional versions of Spearman's rho and related measures of tail dependence
Journal of Multivariate Analysis
On testing equality of intraclass correlations under unequal family sizes
Computational Statistics & Data Analysis
Exact inference on contrasts in means of intraclass correlation models with missing responses
Journal of Multivariate Analysis
A new index to measure positive dependence in trivariate distributions
Journal of Multivariate Analysis
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Spearman's rank-correlation coefficient (also called Spearman's rho) represents one of the best-known measures to quantify the degree of dependence between two random variables. As a copula-based dependence measure, it is invariant with respect to the distribution's univariate marginal distribution functions. In this paper, we consider statistical tests for the hypothesis that all pairwise Spearman's rank correlation coefficients in a multivariate random vector are equal. The tests are nonparametric and their asymptotic distributions are derived based on the asymptotic behavior of the empirical copula process. Only weak assumptions on the distribution function, such as continuity of the marginal distributions and continuous partial differentiability of the copula, are required for obtaining the results. A nonparametric bootstrap method is suggested for either estimating unknown parameters of the test statistics or for determining the associated critical values. We present a simulation study in order to investigate the power of the proposed tests. The results are compared to a classical parametric test for equal pairwise Pearson's correlation coefficients in a multivariate random vector. The general setting also allows the derivation of a test for stochastic independence based on Spearman's rho.