Decompounding discrete distributions: A nonparametric Bayesian approach

Shota Gugushvili, Ester Mariucci, Frank van der Meulen

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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.

Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalScandinavian Journal of Statistics
Publication statusPublished - 2019


  • compound Poisson process
  • data augmentation
  • diophantine equation
  • Gibbs sampler
  • Metropolis-Hastings algorithm
  • Nonparametric Bayesian estimation

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