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X-WR-CALNAME:Department of Statistics
X-ORIGINAL-URL:https://stat.wisc.edu
X-WR-CALDESC:Events for Department of Statistics
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TZID:America/Chicago
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TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20210314T080000
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DTSTART:20211107T070000
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DTSTART;TZID=America/Chicago:20210303T160000
DTEND;TZID=America/Chicago:20210303T170000
DTSTAMP:20210301T080603
CREATED:20210125T152436Z
LAST-MODIFIED:20210218T162911Z
UID:4431-1614787200-1614790800@stat.wisc.edu
SUMMARY:Statistics Seminar
DESCRIPTION:Title: Statistical Exploitation of Unlabeled Data under High Dimensionality \nPresenter: Jiwei Zhao \nAbstract: In this talk\, we consider the benefits of unlabeled data in the semi-supervised setting under high dimensionality\, for parameter estimation and statistical inference. In particular\, we address the following two important questions. First\, can we use the labeled data as well as the unlabeled data to construct a semi-supervised estimator such that its convergence rate is faster than the supervised estimator? Second\, can we construct confidence intervals or hypothesis tests that are guaranteed to be more efficient or powerful than the supervised estimator? We show that\, the semi-supervised estimator with a faster convergence rate exists under some conditions\, and the implementation of this optimal estimator needs a reasonably good estimation of the conditional mean function. For statistical inference\, we mainly propose a safe approach that is guaranteed to be no worse than the supervised estimator in terms of statistical efficiency. Not surprisingly\, if the conditional mean function is well estimated\, our safe approach becomes the semi-parametrically efficient approach. After the theory development\, I will also present some simulation results as well as a real data analysis. \nLink: https://uwmadison.zoom.us/j/94724709216
URL:https://stat.wisc.edu/event/statistics-seminar-16-2021-03-03/
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