Stochastic decomposition for $\ell_p$-norm symmetric survival functions on the positive orthant

TL;DR

This paper introduces a stochastic representation for probability distributions with -norm symmetric survival functions on the positive orthant, enabling efficient simulation and extending previous results to all p .

Contribution

It provides a novel stochastic decomposition for -norm symmetric survival functions, facilitating simulation algorithms for these distributions and max-infinitely divisible laws.

Findings

Efficient simulation algorithm for -norm symmetric survival functions.

Exact simulation method for max-infinitely divisible distributions with -norm symmetry.

Generalization of previous results from p=1 to all p .

Abstract

We derive a stochastic representation for the probability distribution on the positive orthant $(0,\infty)^d$ whose association between components is minimal among all probability laws with $\ell_p$-norm symmetric survival functions. It is given by a transformation of a uniform distribution on the standard unit simplex that is multiplied with an independent finite mixture of certain beta distributions and an additional atom at unity. On the one hand, this implies an efficient simulation algorithm for arbitrary probability laws with $\ell_p$-norm symmetric survival function. On the other hand, this result is leveraged to construct an exact simulation algorithm for max-infinitely divisible probability distributions on the positive orthant whose exponent measure has $\ell_p$-norm symmetric survival function. Both applications generalize existing results for the case $p=1$ to the case of arbitrary $p \geq 1$.