Applications of machine learning to some statistical inference problems
Abstract/Contents
- Abstract
- With impressive statistical learning techniques, new solutions can be provided to challenging problems in statistics. Statistical questions regarding heterogeneous treatment effects and conditional densities are two such examples. Heterogeneous treatment effects describe the influence of a drug or policy with an emphasis on individual variability. I explored the possibilities of applying machine learning tools to the estimation, inference, and accuracy assessment of heterogeneous treatment effects. Conditional densities characterize how a response depends on a set of covariates and extends conditional means to incorporate information like scale and shape. I developed a series of statistical methods for the conditional density estimation borrowing strengths from decision trees and gradient boosting. Despite the maturity of existing machine learning algorithms, careful modifications are required to adapt standard approaches to non-classification and non-prediction queries with desirable statistical properties.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Gao, Zijun |
|---|---|
| Degree supervisor | Hastie, Trevor |
| Thesis advisor | Hastie, Trevor |
| Thesis advisor | Tibshirani, Robert |
| Thesis advisor | Wager, Stefan |
| Degree committee member | Tibshirani, Robert |
| Degree committee member | Wager, Stefan |
| Associated with | Stanford University, Department of Statistics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zijun Gao. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/hb677ff7601 |
Access conditions
- Copyright
- © 2022 by Zijun Gao
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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