Decompounding discrete distributions

A nonparametric Bayesian approach

Journal Article (2019)
Author(s)

Shota Gugushvili (Wageningen University & Research)

Ester Mariucci (University of Potsdam)

F.H. Van Der Meulen (TU Delft - Statistics)

Research Group
Statistics
Copyright
© 2019 Shota Gugushvili, Ester Mariucci, F.H. van der Meulen
DOI related publication
https://doi.org/10.1111/sjos.12413
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Shota Gugushvili, Ester Mariucci, F.H. van der Meulen
Research Group
Statistics
Issue number
2
Volume number
47 (2020)
Pages (from-to)
464-492
Reuse Rights

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Abstract

Suppose that a compound Poisson process is observed discretely in time and assume that its jump distribution is supported on the set of natural numbers. In this paper we propose a nonparametric Bayesian approach to estimate the intensity of the underlying Poisson process and the distribution of the jumps. We provide a Markov chain Monte Carlo scheme for obtaining samples from the posterior. We apply our method on both simulated and real data examples, and compare its performance with the frequentist plug-in estimator proposed by Buchmann and Grübel. On a theoretical side, we study the posterior from the frequentist point of view and prove that as the sample size n→∞, it contracts around the “true,” data-generating parameters at rate 1/√n, up to a n factor.