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Constructing confidence intervals of extreme rainfall quantiles using Bayesian,bootstrap,and profile likelihood approaches

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Science China(Technological Sciences) 2016年第4期59卷 573-585页

作者： CHEN Si LI YaXing shin jiyae KIM TaeWoong department of civil and environmental engineering hanyang universitySeoul 133-791Korea department of electronics&communication engineering hanyang universityAnsan 426-791Korea department of civil and environmental engineering hanyang universityAnsan 426-791Korea

Hydrological risk is highly dependent on the occurrence of extreme *** fact has led to a wide range of studies on the estimation and uncertainty analysis of the *** most cases,confidence intervals(CIs)are constructed to represent the uncertainty of the *** the accuracy of CIs depends on the asymptotic normality of the data and is questionable with limited observations in practice,a Bayesian highest posterior density(HPD)interval,bootstrap percentile interval,and profile likelihood(PL)interval have been introduced to analyze the uncertainty that does not depend on the normality ***,comparison studies to investigate their performances in terms of the accuracy and uncertainty of the estimates are *** addition,the strengths,weakness,and conditions necessary for performing each method also must be ***,in this study,test experiments with simulations from varying parent distributions and different sample sizes were ***,applications to the annual maximum rainfall(AMR)time series data in South Korea were *** districts with 38-year(1973–2010)AMR observations were fitted by the three aforementioned methods in the *** both the experimental and application results,the Bayesian method is found to provide the lowest uncertainty of the design level while the PL estimates generally have the highest accuracy but also the largest *** bootstrap estimates are usually inferior to the other two methods,but can perform adequately when the distribution model is not heavy-tailed and the sample size is *** distribution tail behavior and the sample size are clearly found to affect the estimation accuracy and *** study presents a comparative result,which can help researchers make decisions in the context of assessing extreme rainfall uncertainties.

关键词： Bayesian bootstrap profile likelihood confidence interval frequency analysis

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