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Article

Clustering for Regional Time Trend in the Nonstationary Extreme Distribution

1
Department of Statistics, University of Seoul, Seoul 02504, Korea
2
Department of Statistics, Seoul National University, Seoul 02504, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Maria Mimikou
Water 2022, 14(11), 1720; https://doi.org/10.3390/w14111720
Received: 27 March 2022 / Revised: 16 May 2022 / Accepted: 19 May 2022 / Published: 27 May 2022
(This article belongs to the Section Hydrology)
Since the estimation of tail properties requires a stationarity of observations, it is necessary to develop a de-trending method not dependent on underlying distributions for nonstationary hydrological processes. Moreover, de-trending has been independently applied to hydrological processes, even though the processes are observed in geometrically adjacent sites. This paper presents a distribution-free de-trending method for nonstationary hydrological processes. Our method also provides clustered regional trends obtained by sparse regularization in a general distribution. It aggregates the parameter estimation and clustering within a unified framework. In the simulation study, our proposed method has superiority over other compared methods with respect to MSE and variance of coefficients. In real data analysis, the clustered trends of the annual maximum precipitation in the South Korean peninsula are reported, and the patterns of the estimated trends are visualized. View Full-Text
Keywords: clustering; fused lasso; nonstationary distribution; regional frequency analysis; time trend estimation clustering; fused lasso; nonstationary distribution; regional frequency analysis; time trend estimation
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MDPI and ACS Style

Hong, S.; Jeon, J.-J.; Kim, Y. Clustering for Regional Time Trend in the Nonstationary Extreme Distribution. Water 2022, 14, 1720. https://doi.org/10.3390/w14111720

AMA Style

Hong S, Jeon J-J, Kim Y. Clustering for Regional Time Trend in the Nonstationary Extreme Distribution. Water. 2022; 14(11):1720. https://doi.org/10.3390/w14111720

Chicago/Turabian Style

Hong, Sungchul, Jong-June Jeon, and Yongdai Kim. 2022. "Clustering for Regional Time Trend in the Nonstationary Extreme Distribution" Water 14, no. 11: 1720. https://doi.org/10.3390/w14111720

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