Van Dantzig Seminar

nationwide series of lectures in statistics


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New: Upcoming seminars in Spring 2017 announced.

Next Van Dantzig Seminar: 6 April 2017

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

14:00 - 14:05 Opening
14:05 - 15:05 Tatyana Krivobokova (Göttingen University)
15:05 - 15:25 Break
15:25 - 16:25 Botond Szabó (Leiden University and Budapest University of Technology and Economics)
16:30 - 17:30 Reception
Location: Delft University of Technology, Gebouw 31 TPM, Jaffalaan 5, Lecture Hall A
(Check directions for Gebouw 31 TPM, unusual location)

Titles and abstracts

  • Tatyana Krivobokova

    Kernel partial least squares for stationary data

    We consider the kernel partial least squares algorithm for the solution of nonparametric regression problems when the data are stationary time series. Probabilistic convergence rates of the kernel partial least squares estimator to the true regression function are established under a source condition. The impact of long range dependence in the data is studied both theoretically and in simulations. A real data example on protein dynamics illustrates the approach.

    This is the joint work with Marco Singer and Axel Munk.

  • Botond Szabó

    An asymptotic analysis of nonparametric divide-and-conquer methods

    In the recent years in certain applications datasets have become so large that it becomes unfeasible, or computationally undesirable, to carry out the analysis on a single machine. This gave rise to divide-and-conquer algorithms where the data is distributed over several "local" machines and the computations are done on these machines parallel to each other. Then the outcome of the local computations are somehow aggregated to a global result in a central machine.

    Over the years various divide-and-conquer algorithms were proposed, many of them with limited theoretical underpinning. First we compare the theoretical properties of a (not complete) list of proposed methods on the benchmark nonparametric signal-in-white-noise model. Most of the investigated algorithms use information on aspects of the underlying true signal (for instance regularity), which is usually not available in practice. A central question is whether one can tune the algorithms in a data-driven way, without using any additional knowledge about the signal. We show that (a list of) standard data-driven techniques (both Bayesian and frequentist) can not recover the underlying signal with the minimax rate. This, however, does not imply the non-existence of an adaptive distributed method.

    To address the theoretical limitations of data-driven divide-and-conquer algorithms we consider a setting where the amount of information sent between the local and central machines is expensive and limited. We show that it is not possible to construct data-driven methods which adapt to the unknown regularity of the underlying signal and at the same time communicates the optimal amount of information between the machines.


Supported by




BTK, Amsterdam 2017