Decompounding discrete distributions: A nonparametric Bayesian approach

Shota Gugushvili, Ester Mariucci*, Frank van der Meulen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
33 Downloads (Pure)


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)464-492
Number of pages29
JournalScandinavian Journal of Statistics
Volume47 (2020)
Issue number2
Publication statusPublished - 2019


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


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