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DTSTART:20070311T020000
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DTSTAMP:20240923T142700
DTSTART;TZID=America/Detroit:20241004T100000
DTEND;TZID=America/Detroit:20241004T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Runze Li\, Eberly Family Chair Professor in Statistic\, Penn State University
DESCRIPTION:Abstract:  My talk aims to introduce an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation\, we propose a new test statistic and show that its asymptotic distribution is chi-square distribution whose degree of freedom does not depend on the unknown population distribution. We further conduct power\nanalysis under local alternative hypotheses. In addition\, we study how to control the false discovery rate of the proposed chi-square tests\, which are correlated\, to identify important predictors under a model-free framework. To this end\, we propose a multiple testing procedure and establish its theoretical guarantees. Monte Carlo simulation studies are conducted to assess the performance of the proposed tests and an empirical analysis of a real-world data set is used to illustrate the proposed  methodology.\n\nhttps://science.psu.edu/stat/people/ril4
UID:124536-21853166@events.umich.edu
URL:https://events.umich.edu/event/124536
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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