Van Dantzig Seminar

nationwide series of lectures in statistics


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Next Van Dantzig Seminar: November 29, 2019

Speakers: Gareth Roberts, Holger Dette


Programme: (click names or scroll down for titles and abstracts)

14:30 - 15:30 Gareth Roberts (Warwick)
15:30 - 16:00 Break
16:00 - 17:00 Holger Dette (Bochum)
17:00 - Reception
Location: Vrije Universiteit Amsterdam, room WN-P647.

Titles and abstracts

  • Gareth Roberts

    Principled subsampling and super-efficiency for Bayesian inference

    This talk will discuss the problem of Bayesian computation for posterior densities which are expensive to compute, typically due to the size of the data set under consideration. While subsampling large data sets is being used effectively for optimisation with large data sets, the problem of fully Bayesian posterior exploration is harder and invariably leads to systematic biases in estimation. Two potential solutions to this problem will be presented. Both have the property that although they both use subsampling, they are examples of so-called “exact approximate” algorithms with no systematic bias. The two algorithms described are the SCaLE algorithm, which works in a framework which combines MCMC and SMC to realise an evanescent Markov process whose quasi-stationary distribution is the target distribution. The second method is an example of Piecewise Deterministic Markov Processes, the so-called Zig-Zag algorithm which also utilises a continuous-time non-reversible Markov process whose stationary distribution is the required target.

    References

    The zig-zag process and super-efficient sampling for Bayesian analysis of big data J Bierkens, P Fearnhead, G Roberts The Annals of Statistics 47 (3), 1288-1320, 2019
    The scalable Langevin exact algorithm: Bayesian inference for big data M Pollock, P Fearnhead, AM Johansen, GO Roberts arXiv preprint arXiv:1609.03436, 2017

  • Holger Dette

    Functional data analysis in the Banach space of continuous functions

    Functional data analysis is typically conducted within the L^2-Hilbert space framework. There is by now a fully developed statistical toolbox allowing for the principled application of the functional data machinery to real-world problems, often based on dimension reduction techniques such as functional principal component analysis. At the same time, there have recently been a number of publications that sidestep dimension reduction steps and focus on a fully functional L^2-methodology. This paper goes one step further and develops data analysis methodology for functional time series in the space of all continuous functions. The work is motivated by the fact that objects with rather different shapes may still have a small L^2-distance and are therefore identified as similar when using an L^2-metric. However, in applications it is often desirable to use metrics reflecting the visualization of the curves in the statistical analysis. The methodological contributions are focused on developing two-sample and change-point tests as well as confidence bands, as these procedures appear to be conducive to the proposed setting. Particular interest is put on relevant differences; that is, on not trying to test for exact equality, but rather for pre-specified deviations under the null hypothesis.

    Dette, H., Kokot, K., and Aue, A. (2019). Functional data analysis in the banach space of continuous functions. Annals of Statistics, to appear; ArXiv e-print 1710.07781v2.


Van Dantzig Seminar

The Van Dantzig seminar is a nationwide series of lectures in statistics, which features renowned international and local speakers, from the full width of the statistical sciences. The name honours David van Dantzig (1900-1959), who was the first modern statistician in the Netherlands, and professor in the "Theory of Collective Phenomena" (i.e. statistics) in Amsterdam. The seminar will convene 4 to 6 times a year at varying locations, and is supported financially by among others the STAR cluster and the Section Mathematical Statistics of the VVS-OR.

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