Show simple item record

dc.contributor.advisorWang, Haonan
dc.contributor.authorTu, Yan
dc.contributor.committeememberBreidt, F. Jay
dc.contributor.committeememberChapman, Phillip
dc.contributor.committeememberLuo, J. Rockey
dc.date.accessioned2016-01-11T15:13:52Z
dc.date.available2016-01-11T15:13:52Z
dc.date.issued2015
dc.description2015 Summer.
dc.description.abstractVarying coefficient models are widely used for analyzing longitudinal data. Various methods for estimating coefficient functions have been developed over the years. We revisit the problem under the theme of functional sparsity. The problem of sparsity, including global sparsity and local sparsity, is a recurrent topic in nonparametric function estimation. A function has global sparsity if it is zero over the entire domain, and it indicates that the corresponding covariate is irrelevant to the response variable. A function has local sparsity if it is nonzero but remains zero for a set of intervals, and it identifies an inactive period of the corresponding covariate. Each type of sparsity has been addressed in the literature using the idea of regularization to improve estimation as well as interpretability. In this dissertation, a penalized estimation procedure has been developed to achieve functional sparsity, that is, simultaneously addressing both types of sparsity in a unified framework. We exploit the property of B-spline approximation and group bridge penalization. Our method is illustrated in simulation study and real data analysis, and outperforms the existing methods in identifying both local sparsity and global sparsity. Asymptotic properties of estimation consistency and sparsistency of the proposed method are established. The term of sparsistency refers to the property that the functional sparsity can be consistently detected.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierTu_colostate_0053A_13332.pdf
dc.identifier.urihttp://hdl.handle.net/10217/170359
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019 - CSU Theses and Dissertations
dc.rightsCopyright of the original work is retained by the author.
dc.titlePenalized estimation procedure for varying coefficient models, A
dc.typeText
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record