Searching for structured interactions in and between datasets
Abstract/Contents
- Abstract
- This dissertation considers two problems related to finding structured interactions in and between datasets. The first problem is to estimate interaction terms in a regression where the interaction coefficients are assumed to have a particular low-rank structure. The second problem is to identify relationships between datasets using extensions of recent work on regularized canonical correlation analysis. We propose methodologies and computational strategies based on regularized regression techniques that are well-suited to modern datasets where the number of features may be much larger than the number of observations. The final chapter provides a theoretical result on the performance of a low-rank interaction estimation procedure that motivates its use in large-scale problems.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2012 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Klingenberg, Bradley Jay |
|---|---|
| Associated with | Stanford University, Department of Statistics |
| Primary advisor | Taylor, Jonathan E |
| Thesis advisor | Taylor, Jonathan E |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Walther, Guenther |
| Advisor | Hastie, Trevor |
| Advisor | Walther, Guenther |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bradley Jay Klingenberg. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2012. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2012 by Bradley Jay Klingenberg
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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