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Institut für Stochastik

Kollegiengebäude Mathematik (20.30)
Zimmer 2.056 und 2.002

Karlsruher Institut für Technologie (KIT)
Institut für Stochastik
Englerstr. 2
D-76131 Karlsruhe

Karlsruher Institut für Technologie (KIT)
Institut für Stochastik
Postfach 6980
D-76049 Karlsruhe

Mo-Fr 10:00 - 12:00

Tel.: 0721 608 43270/43265

Fax.: 0721 608 46066

AG Stochastik (Wintersemester 2019/20)

Dozent: Prof. Dr. Nicole Bäuerle, Prof. Dr. Vicky Fasen-Hartmann, Prof. Dr. Tilmann Gneiting, Prof. Dr. Norbert Henze, Prof. Dr. Daniel Hug, Prof. Dr. Günter Last
Veranstaltungen: Seminar (0127200)
Semesterwochenstunden: 2

Studierende und Gäste sind jederzeit herzlich willkommen. Wenn nicht explizit anders unten angegeben, finden alle Vorträge im Seminarraum 2.59 im Mathebau (Gebäude 20.30) statt.

Seminar: Dienstag 15:45-17:15 SR 2.59
Seminarleitung Prof. Dr. Nicole Bäuerle
Sprechstunde: nach Vereinbarung.
Zimmer 2.016 Kollegiengebäude Mathematik (20.30)
Email: nicole.baeuerle@kit.edu
Seminarleitung Prof. Dr. Vicky Fasen-Hartmann
Sprechstunde: Mittwoch 14:00-15:00 Uhr
Zimmer 2.053 Kollegiengebäude Mathematik (20.30)
Email: vicky.fasen@kit.edu
Seminarleitung Prof. Dr. Tilmann Gneiting
Sprechstunde: nach Vereinbarung
Zimmer 2.019 Kollegiengebäude Mathematik (20.30)
Email: tilmann.gneiting@kit.edu
Seminarleitung Prof. Dr. Norbert Henze
Sprechstunde: Dienstag 10:00-11:00 Uhr und nach Vereinbarung.
Zimmer 2.020, Sekretariat 2.002 Kollegiengebäude Mathematik (20.30)
Email: henze@kit.edu
Seminarleitung Prof. Dr. Daniel Hug
Sprechstunde: Nach Vereinbarung.
Zimmer 2.051 Kollegiengebäude Mathematik (20.30)
Email: daniel.hug@kit.edu
Seminarleitung Prof. Dr. Günter Last
Sprechstunde: Dienstag 14:00-15:00 Uhr.
Zimmer 2.001, Sekretariat 2.056 Kollegiengebäude Mathematik (20.30)
Email: guenter.last@kit.edu

Dienstag, 03.12.2019

15:45 Uhr Jose Ameijeiras-Alonso (University of Santiago de Compostela)

Dienstag, 19.11.2019

15:45 Uhr Johannes Bracher

Dienstag, 12.11.2019

15:45 Uhr Prof. Dr. Donald Richards (Pennsylvania State University)

Integral Transform Methods in Goodness-of-Fit Testing for the Wishart Distributions

Abstract: In recent years, random data consisting of positive definite (symmetric or Hermitian) matrices have appeared in several areas of applied research, e.g., diffusion tensor imaging, wireless communication systems, synthetic aperture radar, and volatility models in finance. Given a random sample of such matrices, we wish to test whether the data are drawn from a given distribution. In this talk, we apply the Hankel transform of matrix argument to develop goodness-of-fit tests for the Wishart distributions. The asymptotic distribution of the test statistic is derived in terms of the integrated square of a Gaussian random field, and an explicit formula is obtained for the corresponding covariance operator. The eigenfunctions of the covariance operator are determined explicitly, and the eigenvalues are shown to satisfy certain interlacing properties. Throughout this work, the Bessel functions of matrix argument of Herz (1955) and the zonal polynomials of James (1964) play a crucial role, and the results obtained raise the issue of developing good-ness-of-fit tests for matrix data analogous to the Laplace and Mellin transform-based tests developed by Henze and his co-authors.
(This talk is based on joint work with Elena Hadjicosta.)

Dienstag, 22.10.2019

15:45 Uhr Prof. Dr. Nicholas G. Reich (University of Massachusetts Amherst)

Statistical considerations for probabilistic ensemble forecasts of infectious disease outbreaks

Abstract: Seasonal influenza outbreaks cause substantial annual morbidity and mortality worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. Our team has built a collaborative multi-model probabilistic ensemble forecast of influenza outbreaks in the US, which has been deployed in real-time since 2017. This ensemble model has been optimized to achieve a high log-score, a measure of probabilistic forecast accuracy. In this talk, I will describe the gradual evolution of the model-averaging methods used to build this probabilistic ensemble. We improved upon a simple equal-weighted average of models by estimating model- and target-specific weights using a simple Expectation-Maximization algorithm. In further iterations, we have explored having weights adapt in real-time as a function of recent, in-season perfomance. Weights for this adaptive approach are estimated using a variational inference algorithm of which the EM is a special case. Finally, we are currently exploring methods for transforming individual component forecast models in an attempt to provide better overall calibration and probabilistic accuracy. Our models have consistently achieved near-top rankings in forecasting challenges run by the US Centers for Disease Control and Prevention (CDC) and are used by the CDC for improving situational awareness of governmental health officials and the general public during the influenza season.