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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/5888 |
| Date | 17 September 2007 |
| Creators | Kim, Sinae |
| Contributors | Vannucci, Marina |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 2747270 bytes, electronic, application/pdf, born digital |
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