🤖 AI Summary
This work addresses the absence of a unified theoretical framework for handling conditional quantities and transport functionals across continuous, discrete, and mixed distributions. By constructing a probabilistic framework grounded in distributional derivatives and Dirac delta functions—integrated with copula theory and Fréchet–Hoeffding extremal bounds—the study uncovers the localized mechanisms underlying conditional expectations, risk functions, and related concepts. It introduces, for the first time, Δ-antimonotonic functions to characterize dependence structures. This approach yields sharp bounds on absolute difference moments under fixed marginals and provides concise expressions for the Wasserstein distance, upper-bound transport functionals, and generalized absolute difference moments. Furthermore, it derives novel formulations of the bivariate Gini mean difference and Gini index, successfully applying the framework to normal approximations for Poisson, binomial, and negative binomial distributions.
📝 Abstract
This paper develops a unified probabilistic framework based on distributional derivatives and Dirac delta representations for the analysis of conditional and transportation-related quantities. General identities are established for arbitrary random variables, encompassing absolutely continuous, discrete, and mixed distributions. The proposed approach yields unified formulas for conditional expectations, conditional distributions, hazard functions, and improper distributions, revealing a common localization mechanism underlying these classical concepts.
The framework is further combined with copula methods to investigate transportation and dispersion functionals through dependence structures. Exploiting the extremal properties of the Fréchet--Hoeffding bounds together with expectation inequalities induced by $Δ$-antitonic functions, sharp bounds are derived for absolute difference moments under fixed marginals. These results lead to concise derivations of quantile representations for the Wasserstein distance and a corresponding upper transportation functional, as well as survival-function representations and bounds for generalized absolute difference moments. As a particular case, new representations are obtained for the bivariate Gini mean difference and the associated bivariate Gini index.
Applications are given to Wasserstein-type functionals arising in the normal approximation of standardized counting distributions, including Poisson, Binomial, and Negative Binomial models, for which explicit quantile representations are derived. Overall, the results establish explicit links among conditional structures, dependence modeling, dispersion measures, normal approximation, and optimal transport, providing a unified perspective on several fundamental constructions in probability and mathematical statistics.