Permutation Testing for Monotone Trend
Abstract/Contents
- Abstract
- In this paper, we consider the fundamental problem of testing for monotone trend in a time series. While the term "trend" is commonly used and has an intuitive meaning, it is first crucial to specify its exact meaning in a hypothesis-testing context. A commonly used well-known test is the Mann-Kendall test, which we show does not offer Type 1 error control even in large samples. On the other hand, by an appropriate studentization of the Mann-Kendall statistic, we construct permutation tests that offer asymptotic error control quite generally but retain the exactness property of permutation tests for i.i.d. observations. We also introduce "local" Mann-Kendall statistics as a means of testing for local rather than global trend in a time series. Similar properties of permutation tests are obtained for these tests as well.
Description
| Type of resource | text |
|---|---|
| Publication date | April 4, 2024 |
Creators/Contributors
| Author | Romano, J.P. |
|---|---|
| Author | Tirlea, M.A. |
Subjects
| Subject | hypothesis testing |
|---|---|
| Subject | Mann-Kendall test |
| Subject | time series |
| Genre | Text |
| Genre | Technical report |
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- This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International license (CC BY-NC-ND).
Preferred citation
- Preferred citation
- Romano, J. (2024). Permutation Testing for Monotone Trend. Department of Statistics Technical Report, Stanford University. Available from the Stanford Digital Repository at https://purl.stanford.edu/kf735yx1220. https://doi.org/10.25740/kf735yx1220.
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Statistics Department Technical Reports
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