dc.contributor.advisor | Vannucci, Marina | |
dc.creator | Kim, Sinae | |
dc.date.accessioned | 2007-09-17T19:36:48Z | |
dc.date.available | 2007-09-17T19:36:48Z | |
dc.date.created | 2003-05 | |
dc.date.issued | 2007-09-17 | |
dc.identifier.uri | https://hdl.handle.net/1969.1/5888 | |
dc.description.abstract | The increased collection of high-dimensional data in various fields has raised a strong
interest in clustering algorithms and variable selection procedures. In this disserta-
tion, I propose a model-based method that addresses the two problems simultane-
ously. I use Dirichlet process mixture models to define the cluster structure and to
introduce in the model a latent binary vector to identify discriminating variables. I
update the variable selection index using a Metropolis algorithm and obtain inference
on the cluster structure via a split-merge Markov chain Monte Carlo technique. I
evaluate the method on simulated data and illustrate an application with a DNA
microarray study. I also show that the methodology can be adapted to the problem
of clustering functional high-dimensional data. There I employ wavelet thresholding
methods in order to reduce the dimension of the data and to remove noise from the
observed curves. I then apply variable selection and sample clustering methods in the
wavelet domain. Thus my methodology is wavelet-based and aims at clustering the
curves while identifying wavelet coefficients describing discriminating local features.
I exemplify the method on high-dimensional and high-frequency tidal volume traces
measured under an induced panic attack model in normal humans. | en |
dc.format.extent | 2747270 bytes | en |
dc.format.medium | electronic | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_US | |
dc.publisher | Texas A&M University | |
dc.subject | Bayesian inference | en |
dc.subject | Clustering | en |
dc.subject | Dirichlet process mixture model | en |
dc.subject | DNA microarray data analysis | en |
dc.subject | variable selection | en |
dc.subject | wavelet shrinkage | en |
dc.title | Bayesian variable selection in clustering via dirichlet process mixture models | en |
dc.type | Book | en |
dc.type | Thesis | en |
thesis.degree.department | Statistics | en |
thesis.degree.discipline | Statistics | en |
thesis.degree.grantor | Texas A&M University | en |
thesis.degree.name | Doctor of Philosophy | en |
thesis.degree.level | Doctoral | en |
dc.contributor.committeeMember | Dahl, David B. | |
dc.contributor.committeeMember | Hart, Jeffrey D. | |
dc.contributor.committeeMember | Jayaraman, Arul | |
dc.type.genre | Electronic Dissertation | en |
dc.type.material | text | en |
dc.format.digitalOrigin | born digital | en |