Inference and Prediction for High Dimensional Data via Penalized Regression and Kernel Machine Methods
Analysis of high dimensional data often seeks to identify a subset of important features and assess their effects on the outcome. Furthermore, the ultimate goal is often to build a prediction model with these features that accurately assesses risk for future subjects. Such statistical challenges ari...
| Main Author: | |
|---|---|
| Other Authors: | |
| Language: | en_US |
| Published: |
Harvard University
2012
|
| Subjects: | |
| Online Access: | http://dissertations.umi.com/gsas.harvard:10327 http://nrs.harvard.edu/urn-3:HUL.InstRepos:9367010 |