Model-free methods for multiple testing and predictive inference
Abstract/Contents
- Abstract
- Recent advances in technology have allowed us to collect, store and process an enormous amount of data, bringing unprecedented challenges to interpretable data analysis: first, the structure of data is often complicated, while model assumptions are hard to justify in practice; second, the algorithms used to analyze the data can be extremely complex---think of the convolutional neural nets---making it difficult to develop validity guarantees for the results. Indeed, it has been noticed by researchers that many of the classical statistical methods fail when applied to the modern type of problems---we need a new set of tools to conduct statistical data analysis in the modern era. This dissertation contributes to the toolbox of statistical data analysis in the modern world by presenting several model-free methods for multiple testing and predictive inference. The methods proposed in this dissertation, building upon knockoffs and conformal inference, bypass the modelling of the data structure and the analysis of complex algorithms, and work as wrappers of other (potentially black-box) existing algorithms. Despite the flexibility of these methods, they are guaranteed to achieve statistical validity under the minimal set of assumptions. The validity and efficacy of these methods are evaluated in extensive numerical experiments. Applying these methods to real genetic and clinical data has led to new scientific insights.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ren, Zhimei |
|---|---|
| Degree supervisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Thesis advisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Thesis advisor | Owen, Art B |
| Thesis advisor | Tibshirani, Robert |
| Degree committee member | Owen, Art B |
| Degree committee member | Tibshirani, Robert |
| Associated with | Stanford University, Department of Statistics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zhimei Ren. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/kf427yp0284 |
Access conditions
- Copyright
- © 2021 by Zhimei Ren
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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