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Modern Stochastics: Theory and Applications


Cited by 7
Nonparametric Bayesian inference for multidimensional compound Poisson processes

A non-parametric Bayesian approach to decompounding from high frequency data
Shota Gugushvili, Frank van der Meulen, Peter Spreij
https://doi.org/10.1007/s11203-016-9153-1
Journal:  Statistical Inference for Stochastic Processes Volume 21, Issue 1 (2018), p. 53
Adaptive procedure for Fourier estimators: application to deconvolution and decompounding
Céline Duval, Johanna Kappus
https://doi.org/10.1214/19-EJS1602
Journal:  Electronic Journal of Statistics Volume 13, Issue 2 (2019)
An inverse problem for infinitely divisible moving average random fields
Wolfgang Karcher, Stefan Roth, Evgeny Spodarev, Corinna Walk
https://doi.org/10.1007/s11203-018-9188-6
Journal:  Statistical Inference for Stochastic Processes Volume 22, Issue 2 (2019), p. 263
Bayesian non-parametric inference for $\Lambda$-coalescents: Posterior consistency and a parametric method
Jere Koskela, Paul A. Jenkins, Dario Spanò
https://doi.org/10.3150/16-BEJ923
Journal:  Bernoulli Volume 24, Issue 3 (2018)
Bernstein–von Mises theorems for statistical inverse problems II: compound Poisson processes
Richard Nickl, Jakob Söhl
https://doi.org/10.1214/19-EJS1609
Journal:  Electronic Journal of Statistics Volume 13, Issue 2 (2019)
Decompounding discrete distributions: A nonparametric Bayesian approach
Shota Gugushvili, Ester Mariucci, Frank van der Meulen
https://doi.org/10.1111/sjos.12413
Journal:  Scandinavian Journal of Statistics Volume 47, Issue 2 (2020), p. 464
Nonparametric Bayesian volatility estimation for gamma-driven stochastic differential equations
Denis Belomestny, Shota Gugushvili, Moritz Schauer, Peter Spreij
https://doi.org/10.3150/21-BEJ1413
Journal:  Bernoulli Volume 28, Issue 4 (2022)


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