The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Block bootstrap for Mann–Kendall trend test of serially dependent data
Abstract
Mann–Kendall (MK) test for trend detection must be modified when the data are serially correlated, to prevent the detection of false trends. Various approaches are developed for this purpose, such as prewhitening, trend‐free prewhitening, variance correction and block bootstrap. Each method has its own Type I and Type II errors. In this study, the errors of block bootstrapping MK test are estimated by a simulation study and compared with other methods. Optimal block length that minimizes the Type I error is determined as function of sample size and autocorrelation coefficient. It is shown that the power of block bootstrapping MK test is comparable with those of other modified MK tests. These tests are applied to some annual streamflow series with trend recorded in Turkish rivers, and their powers are compared. A modified form of the trend‐free prewhitening procedure is proposed that has a smaller Type I error. Copyright © 2011 John Wiley & Sons, Ltd.
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“However, such resampling can also be risky in terms of producing false-positive findings if the data is structured (temporal autocorrelation, random effects; e.g., Ihle et al., 2019) and if this structure is not accounted for in the resampling regime (blockwise bootstrap; e.g., Önöz & Bayazit, 2012).”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Önöz & Bayazit, 2012
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Bootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350
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Violating the normality assumption may be the lesser of two evils
Knief, U., & Forstmeier, W. (2021) · Behavior Research Methods · doi:10.3758/s13428-021-01587-5 · Cited by 654
A practice-oriented framework for stationary and nonstationary flood frequency analysis
Vidrio-Sahagún, C. T., Ruschkowski, J., He, J., & Pietroniro, A. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.105940 · Cited by 20
Metadata
- Authors
- Önöz, B., & Bayazit, M.
- Affiliations
- Istanbul Technical University (TR)
- Corresponding author
- Önöz, Bihrat
- Venue
- Hydrological Processes
- Publisher
- Wiley
- Published
- 2012-01-24
- Volume
- 26
- Issue
- 23
- Pages
- 3552-3560
- ISSN
- 0885-6087 (ISSN-L)
- 1099-1085
- OpenAlex
- W1564105742
- Cited by
- 194
- References
- 1
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 12 · 2023: 14 · 2022: 31 · 2021: 19 · 2020: 14 · 2019: 18 · 2018: 11 · 2017: 13 · 2016: 10 · 2015: 8 · 2014: 11 · 2013: 5 · 2012: 1
- FWCI
- 5.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Hydrological Processes (publishedVersion)
- Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Keywords
- Mann-Kendall test, block bootstrap, serial correlation, trend detection, type I error, type II error, autocorrelation, climate change detection
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- CoChat. (2026). Citation check: Block bootstrap for Mann–Kendall trend test of serially dependent data. https://app.cochat.ai/papers/10.1002/hyp.8438
- The paper
- Önöz, B., & Bayazit, M. (2012). Block bootstrap for Mann–Kendall trend test of serially dependent data. Hydrological Processes. https://doi.org/10.1002/hyp.8438
