Permutation-based inference in time series analysis
Abstract/Contents
- Abstract
- Permutation testing is a widely used tool for performing robust and nonparametric inference in a variety of settings. Given observations from a stationary time series, permutation tests allow one to construct exactly level alpha tests under the null hypothesis of an independent and identically distributed (i.i.d.) or, more generally, exchangeable distribution. However, in a general time series setting, the null hypothesis permits distributions which are not i.i.d., in which case permutation tests are not necessarily level alpha, nor are they approximately level alpha in large samples. In this thesis, we present a general framework for restoring the validity of the permutation testing procedure in large samples in a time series context, while retaining the exact rejection probability alpha in finite samples when the observations are i.i.d. Under weak assumptions on the mixing coefficients and moments of the sequence, we show that appropriate studentization of the test statistic leads to asymptotically valid inference in a permutation testing context. In particular, we describe explicitly the permutation testing procedure for three problem applications: testing for dependence in time series, permutation tests based on ordinary least squares regression, and permutation testing for detection of monotone trend.
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 | Tirlea, Marius Aurel |
|---|---|
| Degree supervisor | Romano, Joseph P, 1960- |
| Thesis advisor | Romano, Joseph P, 1960- |
| Thesis advisor | Duchi, John |
| Thesis advisor | Owen, Art B |
| Degree committee member | Duchi, John |
| Degree committee member | Owen, Art B |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Department of Statistics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Marius Tirlea. |
|---|---|
| Note | Submitted to the Department of Statistics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/dn125rm8981 |
Access conditions
- Copyright
- © 2023 by Marius Aurel Tirlea
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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