Goodness-of-Fit Testing for Copulas: A Distribution-Free Approach
Statistics Theory
2018-12-20 v2 Statistics Theory
Abstract
Consider a random sample from a continuous multivariate distribution function with copula . In order to test the null hypothesis that belongs to a certain parametric family, we construct an empirical process on the unit hypercube that converges weakly to a standard Wiener process under the null hypothesis. This process can therefore serve as a `tests generator' for asymptotically distribution-free goodness-of-fit testing of copula families. We also prove maximal sensitivity of this process to contiguous alternatives. Finally, we demonstrate through a Monte Carlo simulation study that our approach has excellent finite-sample performance, and we illustrate its applicability with a data analysis.
Keywords
Cite
@article{arxiv.1710.11504,
title = {Goodness-of-Fit Testing for Copulas: A Distribution-Free Approach},
author = {Sami Umut Can and John H. J. Einmahl and Roger J. A. Laeven},
journal= {arXiv preprint arXiv:1710.11504},
year = {2018}
}