Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/35238
Title: Feature Selection in High-Dimensional Studies
Authors: LUO SHAN
Keywords: feature selection, high dimensional, selection consistency
Issue Date: 20-Jul-2012
Source: LUO SHAN (2012-07-20). Feature Selection in High-Dimensional Studies. ScholarBank@NUS Repository.
Abstract: This thesis comprises two topics: the selection consistency of the extended Bayesian Information Criteria (EBIC) and the sequential LASSO procedure in feature selection under small-n-large-p situation in high-dimensional studies. In the first part of this thesis, we expand the current study of the EBIC to more flexible models. We investigate the properties of EBIC for linear regression models with diverging number of parameters, generalized linear regression models with non-canonical links as well as Cox's proportional hazards model. The conditions under which the EBIC remains selection consistent are established and extensive numerical study results are provided. In the second part of this thesis, we propose a new stepwise selection procedure, sequential LASSO, to conduct feature selection in ultra-high dimensional feature space. The conditions for its selection consistency and sure screening property are explored. The comparison between sequential LASSO and its competitors is provided from both theoretical and computational aspects.
URI: http://scholarbank.nus.edu.sg/handle/10635/35238
Appears in Collections:Ph.D Theses (Open)

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