BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Statistics - ECPv4.9.9//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Statistics
X-ORIGINAL-URL:https://stat.wisc.edu
X-WR-CALDESC:Events for Department of Statistics
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20190310T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20191103T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20191113T160000
DTEND;TZID=America/Chicago:20191113T170000
DTSTAMP:20191213T085505
CREATED:20190724T193358Z
LAST-MODIFIED:20191108T152504Z
UID:1763-1573660800-1573664400@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: Robust Statistical Procedures for Noisy\, High-dimensional Data \nPresenter: Anna Little \nAbstract: This talk addresses two topics related to robust statistical procedures for analyzing noisy\, high-dimensional data: (I) path-based spectral clustering and (II) robust multi-reference alignment. Both methods must overcome a large ambient dimension and lots of noise to extract the relevant low dimensional data structure in a computationally efficient way. In (I)\, the goal is to partition the data into meaningful groups\, and this is achieved by a novel approach which combines a data driven metric with graph-based clustering. Using a data driven metric allows for strong theoretical guarantees and fast algorithms when clusters concentrate around low-dimensional sets. In (II)\, the goal is to recover a hidden signal from many noisy observations of the hidden signal\, where each noisy observation includes a random translation\, a random dilation\, and high additive noise. A wavelet based approach is used to apply a data-driven\, nonlinear unbiasing procedure\, so that the estimate of the hidden signal is robust to high frequency perturbations. \n \nAnna Little Seminar \n
URL:https://stat.wisc.edu/event/statistics-seminar-2019-11-13/
LOCATION:140 Bardeen
CATEGORIES:Seminar
END:VEVENT
END:VCALENDAR