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dc.contributor.advisorLiang, Ye
dc.contributor.authorYe, Shangyuan
dc.date.accessioned2019-03-25T21:59:36Z
dc.date.available2019-03-25T21:59:36Z
dc.date.issued2018-05-01
dc.identifier.urihttps://hdl.handle.net/11244/317780
dc.description.abstractThis dissertation mainly presents a novel Bayesian method for sparse functional data. Specifically, two models are proposed, one of which models all individual functions with a common smoothness and the other groups individual functions with heterogeneous smoothness. The proposed method utilizes the mixed effects model representation of the penalized splines for both the mean function and the individual functions. Given noninformative or weakly informative priors, Bayesian inference on the proposed models are developed and computations are done by using Markov Chain Monte Carlo (MCMC) methods. It has been shown that the proposed Bayesian methods perform well on irregularly spaced sparse functional data, where a traditional mixed eects model may often fail. This dissertation also includes a small section onorthogonal series functional estimation for density functions.
dc.formatapplication/pdf
dc.languageen_US
dc.rightsCopyright is held by the author who has granted the Oklahoma State University Library the non-exclusive right to share this material in its institutional repository. Contact Digital Library Services at lib-dls@okstate.edu or 405-744-9161 for the permission policy on the use, reproduction or distribution of this material.
dc.titleBayesian Analysis for Sparse Functional Data
dc.contributor.committeeMemberZhu, Lan
dc.contributor.committeeMemberHabiger, Joshua Daniel
dc.contributor.committeeMemberGoad, Carla Lynn
dc.contributor.committeeMemberDelen, Dursun
osu.filenameYe_okstate_0664D_15712.pdf
osu.accesstypeOpen Access
dc.description.departmentStatistics
dc.type.genreDissertation
dc.type.materialtext


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