Bayesian variable selection in clustering via dirichlet process mixture models /
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| Format: | Thesis eBook |
| Language: | English |
| Published: |
[College Station, Tex.] :
[Texas A&M University],
[2007]
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| Subjects: | |
| Online Access: | Link to OAK Trust copy |
| 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. |
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| Item Description: | "Major Subject: Statistics" Title from author supplied metadata (automated record created on Nov. 2, 2007.) Vita. Abstract. Electronic resource. |
| Format: | Mode of access: World Wide Web. System requirements: World Wide Web access and Adobe Acrobat Reader. |
| Bibliography: | Includes bibliographical references. |