Decompounding with unknown noise through several independents channels

TL;DR

This paper addresses the challenge of estimating jump intensities in compound Poisson processes with unknown noise, introducing new Fourier and Mellin-based methods for deconvolution and decompounding, supported by adaptive procedures and numerical validation.

Contribution

It presents novel Fourier and Mellin estimators for decompounding with unknown noise, along with adaptive cutoff selection methods and numerical demonstrations.

Findings

Effective Fourier estimator for jump density with controlled mean squared error

Adaptive cutoff selection improves estimator performance

Numerical results validate the proposed methods

Abstract

In this article, we consider two different statistical models. First, we focus on the estimation of the jump intensity of a compound Poisson process in the presence of unknown noise. This problem combines both the deconvolution problem and the decompounding problem. More specifically, we observe several independent compound Poisson processes but we assume that all these observations are noisy due to measurement noise. We construct an Fourier estimator of the jump density and we study its mean integrated squared error. Then, we propose an adaptive method to correctly select the cutoff of the estimator and we illustrate the efficiency of the method with numerical results. Secondly, we introduce in this paper the multiplicative decompounding problem. We study this problem with Mellin density estimators. We develop an adaptive procedure to select the optimal cutoff parameter.