University of Cambridge > > CCIMI Seminars > Adaptive and robust nonparametric Bayesian contraction rates for discretely observed compound Poisson processes

Adaptive and robust nonparametric Bayesian contraction rates for discretely observed compound Poisson processes

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  • UserDr Alberto J. Coca
  • ClockWednesday 07 November 2018, 14:00-15:00
  • HouseCMS, MR14.

If you have a question about this talk, please contact J.W.Stevens.

Compound Poisson processes (CPPs) are the textbook example of pure jump stochastic processes, and they approximate arbitrarily well much richer classes of processes such as Lévy processes. They are characterised by the so-called Lévy jump distribution, N, driving the frequency at which jumps (randomly) occur and their (random) sizes. Hence, they provide a simple, yet fundamental, model for random shocks in a system applied in a myriad of problems within natural sciences, engineering and economics. In most applications, the underlying CPP is not perfectly observed: only discrete observations over a finite-time interval are available. Thus, the process may jump several times between two observations and we are effectively observing a random variable corrupted by a sum of a random number of copies of itself. Consequently, estimating N is a non-linear statistical inverse problem.

In the recent years, understanding the frequentist asymptotic behaviour of the Bayesian method in inverse problems and, in particular, in this problem has received considerable attention. In this talk, we will present ongoing results on posterior contraction rates for the nonparametric density \nu of N: we show two-sided stability estimates that guarantee that the classical theory in Ghosal, Ghosh, van der Vaart (2000) can be transferred to our problem, allowing us to use mixture and Gaussian priors for \nu multidimensional; furthermore, the rates are robust to the observation interval, i.e. optimal adaptive inference can be made without specification of whether the regime is of high- or low-frequency; and, lastly, we propose an efficient \infty-MCMC procedure to draw from the posterior for infinite dimensional priors. Given the diversity of the CCIMI members, we will attempt to introduce all these concepts during the presentation.

This talk is part of the CCIMI Seminars series.

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