Bayesian semiparametric modelling of phase-varying point processes. (English) Zbl 07524979
Summary: We propose a Bayesian semiparametric approach for registration of multiple point processes. Our approach entails modelling the mean measures of the phase-varying point processes with a Bernstein-Dirichlet prior, which induces a prior on the space of all warp functions. Theoretical results on the support of the induced priors are derived, and posterior consistency is obtained under mild conditions. Numerical experiments suggest a good performance of the proposed methods, and a climatology real-data example is used to showcase how the method can be employed in practice.
MSC:
| 62-XX | Statistics |
Keywords:
Bernstein-Dirichlet prior; Fréchet mean; phase variation; point processes; random Bernstein polynomials; Wasserstein distance; Bernstein-Dirichlet priorSoftware:
fda (R)References:
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