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제목
[통계연구소 세미나] 3월 13일 오전 11시 신민석 박사 (Harvard 대학)
작성일
2018.03.08
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응용통계학과
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제목 : Functional Horseshoe Priors for Subspace Shrinkage

연사 : Harvard 대학 신민석 교수

일정 : 3월 13일 화요일 오전 11시

Abstract : We introduce a new shrinkage prior on function spaces, the functional horseshoe
prior, that encourages shrinkage towards parametric classes of functions. Unlike ex-
isting shrinkage priors for parametric models, the shrinkage acts on the shape of
the function rather than sparsity of model parameters. We theoretically exhibit the
efficacy of the proposed approach by showing an adaptive posterior concentration
property on the function. We show the consistency of the model selection procedure
that thresholds the shrinkage parameter of the functional horseshoe prior. We apply
the proposed prior to nonparametric additive models. We compare its performance
with the procedure based on the standard horseshoe prior and a number of penal-
ized likelihood approaches, and the proposed procedure achieves smaller estimation
error and more accurate model selection, compared to the other procedures in the
considered simulated and real examples. The proposed prior also provides a natu-
ral penalization interpretation, and casts light on a new class of penalized likelihood
methods.

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2018년 3월 13일_Minsuk_Shin.pdf