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X-WR-CALNAME:Centre for Mathematical Sciences
X-ORIGINAL-URL:https://math-sciences.org
X-WR-CALDESC:Events for Centre for Mathematical Sciences
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TZID:UTC
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TZOFFSETFROM:+0000
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DTSTART:20190101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191016T150000
DTEND;TZID=UTC:20191016T160000
DTSTAMP:20230528T191323
CREATED:20190621T175905Z
LAST-MODIFIED:20191001T134722Z
UID:3638-1571238000-1571241600@math-sciences.org
SUMMARY:Cristiano Villa (University of Kent)
DESCRIPTION:Title: A Loss-Based Prior for Variable Selection in Linear Regression Methods \nAbstract: In this work we propose a novel model prior for variable selection in linear regression. The idea is to determine the prior mass by considering the worth of each of the regression models\, given the number of possible covariates under consideration. The worth of a model consists of the information loss and the loss due to model complexity. While the information loss is determined objectively\, the loss expression due to model complexity is flexible and\, the penalty on model size can be even customized to include some prior knowledge. Some versions of the loss-based prior are proposed and compared empirically. Through simulation studies and real data analyses\, we compare the proposed prior to the Scott and Berger prior\, for noninformative scenarios\, and with the Beta-Binomial prior\, for informative scenarios.
URL:https://math-sciences.org/event/cristiano-villa-university-of-kent/
LOCATION:Rolle 116
CATEGORIES:Seminars,Statistics and Data Science
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