Pseudolikelihood methods for robust graphical model selection
Abstract/Contents
- Abstract
- Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply 1-norm penalties to either parametric likelihoods, or, regularized regression/pseudo-likelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity. This thesis proposes new pseudo-likelihood based graphical model selection methods that aims to overcome some of the shortcomings of current methods, but at the same time retain their respective strengths. We also present a novel unifying framework that places all graphical pseudo-likelihood methods as special cases of a more general formulation, leading to significant insights. This thesis also develops a comprehensive framework for analyzing gene network associations in case-control studies with an application in cardiovascular biology. Furthermore, demonstrations of successfully applying the proposed methodologies to analyzing financial as well as gene expression datasets are provided.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Oh, Sang-Yun |
|---|---|
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. |
| Primary advisor | Rajaratnam, Balakanapathy |
| Thesis advisor | Rajaratnam, Balakanapathy |
| Thesis advisor | Friedman, J. H. (Jerome H.) |
| Thesis advisor | Khare, Kshitij |
| Advisor | Friedman, J. H. (Jerome H.) |
| Advisor | Khare, Kshitij |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Sang-Yun Oh. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Sang-Yun Oh
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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