Bernstein von Mises theorems for statistical inverse problems II: Compound Poisson processes

Richard Nickl, Jakob Söhl

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)
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Abstract

We study nonparametric Bayesian statistical inference for the parameters governing a pure jump process of the form (Formula Presented) where N(t) is a standard Poisson process of intensity λ, and Z k are drawn i.i.d. from jump measure μ. A high-dimensional wavelet series prior for the Lévy measure ν = λμ is devised and the posterior distribution arises from observing discrete samples Y Δ, Y , …, Y at fixed observation distance Δ, giving rise to a nonlinear inverse inference problem. We derive contraction rates in uniform norm for the posterior distribution around the true Lévy density that are optimal up to logarithmic factors over Hölder classes, as sample size n increases. We prove a functional Bernstein–von Mises theorem for the distribution functions of both μ and ν, as well as for the intensity λ, establishing the fact that the posterior distribution is approximated by an infinite-dimensional Gaussian measure whose covariance structure is shown to attain the information lower bound for this inverse problem. As a consequence posterior based inferences, such as nonparametric credible sets, are asymptotically valid and optimal from a frequentist point of view.

Original languageEnglish
Pages (from-to)3513–3571
Number of pages59
JournalElectronic Journal of Statistics
Volume13
Issue number2
DOIs
Publication statusPublished - 2019

Keywords

  • Bayesian nonlinear inverse problems
  • compound Poisson processes
  • L´evy processes
  • asymptotics of nonparametric Bayes procedures
  • Compound Poisson processes
  • Asymptotics of nonparametric Bayes procedures
  • Lévy processes

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