Decompounding on Compact Symmetric Spaces

TL;DR

This paper introduces a new estimator for the decompounding problem on compact symmetric spaces, achieving optimal convergence rates that depend on the space's rank, extending previous work on Lie groups.

Contribution

It extends harmonic analysis-based deconvolution methods to compact symmetric spaces, providing convergence rates and optimality results that depend on the space's rank.

Findings

Estimator converges in mean squared error

Convergence rates match Euclidean density estimation bounds

Optimality depends on the rank of the symmetric space

Abstract

This paper examines a stochastic deconvolution problem on compact symmetric spaces which is referred to as decompounding. This involves estimating the step distributions of a random walk, where in addition the number of steps between observations is unknown. The harmonic analysis of symmetric spaces is used to construct an estimator to the problem which converges in mean squared error, extending and improving on the analogous problem on compact Lie groups. The rates of convergence are shown to coincide with asymptotic lower bounds of density estimation in Euclidean space. We provide proofs that while the same rates hold for general density estimation problems in compact symmetric spaces, the decompounding problem lies in a subclass of these with different lower bounds depending on the rank of the space. Consequently, the optimality of the estimator depends on the rank of the symmetric space. Decompounding is a broad problem which appears in applications ranging from mathematical finance to wave optics, and the extension to compact symmetric spaces covers manifolds that commonly appear in the statistics literature.