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Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Institut für Mathematik

Forschungsseminar Mathematische Statistik

Für den Bereich Statistik

S. Greven, W. Härdle, M. Reiß, V. Spokoiny



Weierstrass-Institut für Angewandte Analysis und Stochastik
Mohrenstrasse 39
10117 Berlin



mittwochs, 10.00 - 12.00 Uhr



Aufgrund der aktuellen Situation finden die Vorträge bis auf Weiteres online unter:    https://zoom.us/j/159082384    statt.
14. April 2021
21. April 2021. 
Hans-Georg Müller (UC Davis)
Rescheduled due to technical problems: to May 26th
26. u. 27 April 2021
1. Victoria Peak Conference, Blockchain Research Center, HU Berlin
28. April 2021
Kein Vortrag
05. Mai 2021
12. Mai 2021
19. Mai 2021
Hannes Leeb (University of Vienna)
A (tight) upper bound for the length of confidence intervals with conditional coverage
Abstract: We show that two popular selective inference procedures, namely data carving (Fithian et al., 2017) and selection with a randomized response (Tian et al., 2018b), when combined with the polyhedral method (Lee et al., 2016), result in confidence intervals whose length is bounded. This contrasts results for confidence intervals based on the polyhedral method alone, whose expected length is typically infinite (Kivaranovic and Leeb, 2020). Moreover, we show that these two procedures always dominate corresponding sample-splitting methods in terms of interval length.
26. Mai 2021    Caution, 9-11 am
Hans-Georg Müller (UC Davis)
Functional Models for Time-Varying Random Objects
Abstract: In recent years, samples of random objects and time-varying object data such as time-varying distributions or networks that are not in a vector space have become increasingly prevalent. Such data can be viewed as elements of a general metric space that lacks local or global linear structure.
Common approaches that have been used with great success for the analysis of functional data, such as functional principal component analysis, are therefore not applicable. The concept of metric covariance makes it possible to define a metric auto-covariance function for a sample of random curves that take values in a general metric space and it can be shown to be non-negative definite when the squared semi-metric of the underlying space is of negative type. Then the eigenfunctions of the linear operator with the auto-covariance function as kernel can be used as building blocks for an object functional principal component analysis, which includes real-valued Frechet scores and metric-space valued object functional principal components. Sample based estimates of these quantities are shown to be asymptotically consistent and are illustrated with various data. (Joint work with Paromita Dubey, Stanford University.)
02. Juni 2021
09. Juni 2021
Victor Panaretos (EPFL Lausanne)
16. Juni 2021    Caution, at 12.30 pm !
Irène Gijbels (KU Leuven)
23. Juni 2021
30. Juni 2021
07. Juli 2021
14. Juli 2021

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Frau Andrea Fiebig

Mail: fiebig@mathematik.hu-berlin.de
Telefon: +49-30-2093-45460
Fax:        +49-30-2093-45451
Humboldt-Universität zu Berlin
Institut für Mathematik
Unter den Linden 6
10099 Berlin, Germany