An Inverse Problem for Infinitely Divisible Moving Average Random Fields

Abstract

Given a low frequency sample of an infinitely divisible moving average random field $\{{\int }_{{\mathbb{R}}^d}f(x-t)\Lambda (dx);t\in {\mathbb{R}}^d\}$ with a known simple function $f$, we study the problem of nonparametric estimation of the Lévy characteristics of the independently scattered random measure $\Lambda$. We provide three methods, a simple plug-in approach, a method based on Fourier transforms and an approach involving decompositions with respect to $L^2$-orthonormal bases, which allow to estimate the Lévy density of $\Lambda$. For these methods, the bounds for the $L^2$-error are given. Their numerical performance is compared in a simulation study.

44 pages, 4 figures