Confidence Interval Estimation for Precipitation Quantiles Based on Principle of Maximum Entropy
| Autor: | Songbai Song, Ting Wei |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010504 meteorology & atmospheric sciences
0208 environmental biotechnology Monte Carlo method General Physics and Astronomy lcsh:Astrophysics 02 engineering and technology 01 natural sciences Article principle of maximum entropy Pome lcsh:QB460-466 Statistics lcsh:Science Extreme value theory Monte Carlo simulation 0105 earth and related environmental sciences Mathematics precipitation frequency analysis Series (mathematics) Principle of maximum entropy Estimator engineering_other lcsh:QC1-999 Confidence interval 020801 environmental engineering confidence interval lcsh:Q quantile estimation lcsh:Physics Quantile |
| Zdroj: | Entropy Volume 21 Issue 3 Entropy, Vol 21, Iss 3, p 315 (2019) |
| DOI: | 10.20944/preprints201901.0164.v1 |
| Popis: | Confidence interval of is an interval corresponding to a specified confidence and including the true value. It can be used to describe the precision of a statistical quantity and quantify its uncertainty. Although the principle of maximum entropy (POME) has been used for a variety of applications in hydrology, the confidence intervals of the POME quantile estimators have not been available. In this study, the calculation formulas of asymptotic variances and confidence intervals of quantiles based on POME for Gamma, Pearson type 3 (P3) and Extreme value type 1 (EV1) distributions were derived. Monte Carlo Simulation experiments were performed to evaluate the performance of derived formulas for finite samples. Using four data sets for annual precipitation at the Weihe River basin in China, the derived formulas were applied for calculating the variances and confidence intervals of precipitation quantiles for different return periods and the results were compared with those of the methods of moments (MOM) and of maximum likelihood (ML) method. It is shown that POME yields the smallest standard errors and the narrowest confidence intervals of quantile estimators among the three methods, and can reduce the uncertainty of quantile estimators |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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