Multivariate Information Measures: A Copula-based Approach
<|reference_start|>Multivariate Information Measures: A Copula-based Approach: Multivariate datasets are common in various real-world applications. Recently, copulas have received significant attention for modeling dependencies among random variables. A copula-based information measure is required to quantify the uncertainty inherent in these dependencies. This paper introduces a multivariate variant of the cumulative copula entropy and explores its various properties, including bounds, stochastic orders, and convergence-related results. Additionally, we define a cumulative copula information generating function and derive it for several well-known families of multivariate copulas. A fractional generalization of the multivariate cumulative copula entropy is also introduced and examined. We present a non-parametric estimator of the cumulative copula entropy using empirical beta copula. Furthermore, we propose a new distance measure between two copulas based on the Kullback-Leibler divergence and discuss a goodness-of-fit test based on this measure.<|reference_end|>
@article{arshad2024multivariate,
    title={Multivariate Information Measures: A Copula-based Approach},
    author={Mohd. Arshad, Swaroop Georgy Zachariah, Ashok Kumar Pathak},
    journal={arXiv preprint arXiv:2408.02028},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02028},
    primaryClass={stat.ME cs.IT math.IT}
}