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Entropy 2019, 21(3), 315; https://doi.org/10.3390/e21030315

Confidence Interval Estimation for Precipitation Quantiles Based on Principle of Maximum Entropy

College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
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Received: 15 January 2019 / Revised: 6 March 2019 / Accepted: 18 March 2019 / Published: 22 March 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

The principle of maximum entropy (POME) has been used for a variety of applications in hydrology, however it has not been used in confidence interval estimation. Therefore, the POME was employed for confidence interval estimation for precipitation quantiles in this study. The gamma, Pearson type 3 (P3), and extreme value type 1 (EV1) distributions were used to fit the observation series. The asymptotic variances and confidence intervals of gamma, P3, and EV1 quantiles were then calculated based on POME. Monte Carlo simulation experiments were performed to evaluate the performance of the POME method and to compare with widely used methods of moments (MOM) and the maximum likelihood (ML) method. Finally, the confidence intervals T-year design precipitations were calculated using the POME for the three distributions and compared with those of MOM and ML. Results show that the POME is superior to MOM and ML in reducing the uncertainty of quantile estimators. View Full-Text
Keywords: principle of maximum entropy; quantile estimation; confidence interval; Monte Carlo simulation; precipitation frequency analysis principle of maximum entropy; quantile estimation; confidence interval; Monte Carlo simulation; precipitation frequency analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wei, T.; Song, S. Confidence Interval Estimation for Precipitation Quantiles Based on Principle of Maximum Entropy. Entropy 2019, 21, 315.

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