Long‐term trend analysis of extreme coastal sea levels with changepoint detection
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DOI: 10.1111/rssc.12466
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References listed on IDEAS
- Lee Fawcett & David Walshaw, 2012. "Estimating return levels from serially dependent extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 23(3), pages 272-283, May.
- Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
- Christopher S. Watson & Neil J. White & John A. Church & Matt A. King & Reed J. Burgette & Benoit Legresy, 2015. "Unabated global mean sea-level rise over the satellite altimeter era," Nature Climate Change, Nature, vol. 5(6), pages 565-568, June.
- David S. Matteson & Nicholas A. James, 2014. "A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 334-345, March.
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