Bayesian latent factor regression for multivariate functional data with variable selection다변량 함수형 자료에 대한 베이지안 잠재요인 회귀 및 변수 선택 모형

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In biomedical research, multivariate functional data (MFD) are frequently encountered. Majority of the existing approaches for functional data analysis focus on univariate functional data (UFD) and the methodology for MFD is far less studied. Particularly, the problem of investigating covariate effects on MFD has received little attention. In this dissertation, we propose a fully Bayesian latent factor regression for studying covariate effects on MFD. The proposed model obtains a low-dimensional representation of MFD through basis expansions for splines and factor analysis for the basis coefficients. Then, the latent factors specific to each functional outcome are regressed onto covariates accounting for residual correlations among multiple outcomes. The assessment of covariate effects is conducted based on the marginal inclusion probability for each covariate, which is calculated a posteriori by assigning a stochastic search variable selection (SSVS) prior to the regression coefficients. To better control for the false discovery rate, we propose a multivariate SSVS prior that allows for a set of coefficients to be zero simultaneously. We illustrate the proposed method through a simulation study and an application to the Japan weather data.
Advisors
Chung, Yeonseungresearcher정연승researcher
Description
한국과학기술원 :수리과학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 수리과학과, 2019.8,[v, 47 p. :]

Keywords

Multivariate functional data▼abayesian latent factor regression▼abasis functions for splines▼astochastic search variable selection▼amultiplicative gamma process shrinkage; 다변량 함수형 자료▼a베이지안 잠재 요인 회귀 모형▼a스플라인 기저 함수▼a확률적 탐색 변수 선택▼a승법적 감마 과정 축소

URI
http://hdl.handle.net/10203/283234
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=871401&flag=dissertation
Appears in Collection
MA-Theses_Ph.D.(박사논문)
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