Zoom meeting

The assumption that high dimensional data is Gaussian is pervasive in many statistical procedures, due to the ease of analytic tractability this special distribution provides. We explore the relaxation of the Gaussian assumption in Shrinkage estimation, Stein's Unbiased Risk Estimate, and Single Index Models, using two tools that originate in Stein's method: Stein kernels, and the zero bias distribution. Taking this approach leads to measures of discrepancy from the Gaussian that arise naturally from the form of the procedures considered, and result in performance bounds that apply in contexts not restricted to the Gaussian. The resulting bounds typically include an additional term that reflects the cost of deviation from the Gaussian, and that vanishes for the Gaussian, thus recovering this paricular special case. (with Xiaohan Wei, Max Fathi, Gesine Reinert, and Adrien Samaurd)

Hörer sollten sich bitte im Moodle-Raum https://moodle.tu-dortmund.de/enrol/index.php?id=23191 des Seminars anmelden. An alle Angemeldeten werden die Zugangsdaten zum meeting geschickt. Weitere Vorträge in der Reihe sind unter http://www.mathematik.tu-dortmund.de/lsix/lehre/WiSe2021/GRK-Seminar/index.php einsehbar.

Larry Goldstein

University of Southern California