Post-selection inference for models characterized by quadratic constraints
Abstract/Contents
- Abstract
- To address the fundamental statistical problem of conducting inference after model selection a recently developed approach conditions on the selected model and uses the corresponding truncated probability laws for inference. Though relatively simple to state, the application of this principle varies in difficulty depending on which model selection procedure is under consideration. This work identifies a general mathematical framework encompassing many model selection procedures. The simple algebra of quadratic constraints allows computation of one-dimensional truncated supports for conditional versions of standard test statistics like the chi-squared and F tests used in regression. Several important examples illustrate the utility of this framework, including forward selection with groups of variables and linear model selection with cross-validation.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Loftus, Joshua Robert |
|---|---|
| Associated with | Stanford University, Department of Statistics. |
| Primary advisor | Taylor, Jonathan |
| Thesis advisor | Taylor, Jonathan |
| Thesis advisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Thesis advisor | Romano, Joseph P, 1960- |
| Thesis advisor | Tibshirani, Robert |
| Advisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Advisor | Romano, Joseph P, 1960- |
| Advisor | Tibshirani, Robert |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Joshua Robert Loftus. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Joshua Loftus
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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