Distributional robustness and minimax optimality in selected problems
Abstract/Contents
- Abstract
- This thesis offers a comprehensive exploration of distributional uncertainties and their implications on machine learning and statistical estimation, providing insights and solutions that are both theoretically sound and practically impactful. We study a diverse array of problem settings, including non-parametric regression, multivariate extreme values theory, and data analytics scenarios involving missing data.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhang, Xuhui, (Researcher in management science and engineering) |
|---|---|
| Degree supervisor | Blanchet, Jose H |
| Thesis advisor | Blanchet, Jose H |
| Thesis advisor | Glynn, Peter W |
| Thesis advisor | Pelger, Markus |
| Degree committee member | Glynn, Peter W |
| Degree committee member | Pelger, Markus |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Management Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Xuhui Zhang. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/zk877rt9747 |
Access conditions
- Copyright
- © 2023 by Xuhui Zhang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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