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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230508T160000
DTEND;TZID=Asia/Seoul:20230508T170000
DTSTAMP:20260426T013454
CREATED:20230425T045600Z
LAST-MODIFIED:20230425T045600Z
UID:7637-1683561600-1683565200@www.ibs.re.kr
SUMMARY:Kyongwon Kim\, On sufficient graphical models
DESCRIPTION:We introduce a sufficient graphical model by applying the recently developed nonlinear sufficient dimension reduction techniques to the evaluation of conditional independence. The graphical model is nonparametric in nature\, as it does not make distributional assumptions such as the Gaussian or copula Gaussian assumptions. However\, unlike a fully nonparametric graphical model\, which relies on the high-dimensional kernel to characterize conditional independence\,  our graphical model is based on conditional independence given a set of sufficient predictors with a substantially reduced dimension. In this way we avoid the curse of dimensionality that comes with a high-dimensional kernel. We develop the population-level properties\,  convergence rate\, and variable selection consistency of our estimate. \nBy simulation comparisons and an analysis of the DREAM 4 Challenge data set\, we demonstrate that our method outperforms the existing methods when the Gaussian or copula Gaussian assumptions are violated\, and its performance remains excellent in the high-dimensional setting.
URL:https://www.ibs.re.kr/bimag/event/kyongwon-kim-on-sufficient-graphical-models/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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