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DTSTART:20070311T020000
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DTSTAMP:20230908T163814
DTSTART;TZID=America/Detroit:20230915T100000
DTEND;TZID=America/Detroit:20230915T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Kean Ming Tan\, Assistant Professor\, Department of Statistics\, University of Michigan
DESCRIPTION:Abstract: The expected shortfall is defined as the average over the tail below (or above) a certain quantile of a probability distribution. The expected shortfall regression provides powerful tools for learning the relationship between a response variable and a set of covariates while exploring the heterogeneous effects of the covariates. In the health disparity research\, for example\, the lower/upper tail of the conditional distribution of a health-related outcome\, given covariates\, is often of importance.  Motivated by the idea of using Neyman-orthogonal scores to reduce sensitivity to nuisance parameters\, we consider a computationally efficient two-step procedure for estimating the expected shortfall regression coefficients.  We establish explicit non-asymptotic bounds on the resulting estimator that lay down the foundation for performing statistical inference under different scenarios: (i) classical setting with $p<n$\; (ii) high-dimensional setting with $p>n$\; and (iii) under heavy-tailed random noise.  \n\nhttp://www.keanmingtan.com/
UID:109423-21822011@events.umich.edu
URL:https://events.umich.edu/event/109423
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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