Bayesian variable selection in clustering via dirichlet process mixture models
| dc.contributor | Vannucci, Marina | |
| dc.creator | Kim, Sinae | |
| dc.date.accessioned | 2007-09-17T19:36:48Z | |
| dc.date.accessioned | 2017-04-07T19:53:28Z | |
| dc.date.available | 2007-09-17T19:36:48Z | |
| dc.date.available | 2017-04-07T19:53:28Z | |
| dc.date.created | 2003-05 | |
| dc.date.issued | 2007-09-17 | |
| 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. | |
| dc.identifier.uri | http://hdl.handle.net/1969.1/5888 | |
| dc.language.iso | en_US | |
| dc.publisher | Texas A&M University | |
| dc.subject | Bayesian inference | |
| dc.subject | Clustering | |
| dc.subject | Dirichlet process mixture model | |
| dc.subject | DNA microarray data analysis | |
| dc.subject | variable selection | |
| dc.subject | wavelet shrinkage | |
| dc.title | Bayesian variable selection in clustering via dirichlet process mixture models | |
| dc.type | Book | |
| dc.type | Thesis |