BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/New_York
TZURL:http://tzurl.org/zoneinfo/America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20190110T150539
DTSTART;TZID=America/New_York:20190122T161000
DTEND;TZID=America/New_York:20190122T173000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Pragya Sur\, Department of Statistics\, Stanford University
DESCRIPTION:Logistic regression is arguably the most widely used and studied non-linear model in statistics. Classical maximum-likelihood theory based statistical inference is ubiquitous in this context. This theory hinges on well-known fundamental results: (1) the maximum-likelihood-estimate (MLE) is asymptotically unbiased and normally distributed\, (2) its variability can be quantified via the inverse Fisher information\, and (3) the likelihood-ratio-test (LRT) is asymptotically a Chi-Squared. In this talk\, I will show that in the common modern setting where the number of features and the sample size are both large and comparable\, classical results are far from accurate. In fact\, (1) the MLE is biased\, (2) its variability is far greater than classical results\, and (3) the LRT is not distributed as a Chi-Square. Consequently\, p-values obtained based on classical theory are completely invalid in high dimensions. In turn\, I will propose a new theory that characterizes the asymptotic behavior of both the MLE and the LRT under some assumptions on the covariate distribution\, in a high-dimensional setting. Empirical evidence demonstrates that this asymptotic theory provides accurate inference in finite samples. Practical implementation of these results necessitates the estimation of a single scalar\, the overall signal strength\, and I will propose a procedure for estimating this parameter precisely. This is based on joint work with Emmanuel Candes and Yuxin Chen.
UID:58282-14452837@events.umich.edu
URL:https://events.umich.edu/event/58282
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 411
CONTACT:
END:VEVENT
END:VCALENDAR