PDE characterisation of geometric distribution functions and quantiles

TL;DR

This paper demonstrates that geometric distribution functions in Euclidean spaces can be explicitly reconstructed via linear PDEs, revealing dimension-dependent behaviors and implications for statistical depth analysis.

Contribution

It introduces a PDE-based framework for reconstructing probability measures from geometric cdfs and explores their regularity and dimension-dependent properties.

Findings

Reconstruction of measures via PDEs is explicit and potentially fractional.

Geometric cdfs can control depth region probabilities.

Reconstruction behavior differs in odd and even dimensions.

Abstract

We show that in any Euclidean space, an arbitrary probability measure can be reconstructed explicitly by its geometric (or spatial) distribution function. The reconstruction takes the form of a (potentially fractional) linear PDE, where the differential operator is given in closed form. This result implies that, contrary to a common belief in the statistical depth community, geometric cdf's in principle provide exact control over the probability content of all depth regions. We present a comprehensive study of the regularity of the geometric cdf, and show that a continuous density in general does not give rise to a geometric cdf with enough regularity to reconstruct the density pointwise. Surprisingly, we prove that the reconstruction displays different behaviours in odd and even dimension: it is local in odd dimension and completely nonlocal in even dimension. We investigate this issue and provide a partial counterpart for even dimensions, and establish a general representation formula of the geometric cdf of spherically symmetric probability laws in odd dimensions. We provide explicit examples of the reconstruction of a density from its geometric cdf in dimension 2 and 3.