Estimation of confidence intervals of quantiles for the Weibull distribution |
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| Authors: | J-H Heo J D Salas K-D Kim |
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| Institution: | (1) Department of Civil Engineering, Yonsei University Seoul, Korea, KR;(2) Department of Civil Engineering, Colorado State University Fort Collins, CO 80523, USA, US;(3) Korea Infrastructure Safety and Technology Cooperation Korea, KR |
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| Abstract: | Estimation of confidence limits and intervals for the two- and three-parameter Weibull distributions are presented based
on the methods of moment (MOM), probability weighted moments (PWM), and maximum likelihood (ML). The asymptotic variances
of the MOM, PWM, and ML quantile estimators are derived as a function of the sample size, return period, and parameters. Such
variances can be used for estimating the confidence limits and confidence intervals of the population quantiles. Except for
the two-parameter Weibull model, the formulas obtained do not have simple forms but can be evaluated numerically. Simulation
experiments were performed to verify the applicability of the derived confidence intervals of quantiles. The results show
that overall, the ML method for estimating the confidence limits performs better than the other two methods in terms of bias
and mean square error. This is specially so for γ≥0.5 even for small sample sizes (e.g. N=10). However, the drawback of the ML method for determining the confidence limits is that it requires that the shape parameter
be bigger than 2. The Weibull model based on the MOM, ML, and PWM estimation methods was applied to fit the distribution of
annual 7-day low flows and 6-h maximum annual rainfall data. The results showed that the differences in the estimated quantiles
based on the three methods are not large, generally are less than 10%. However, the differences between the confidence limits
and confidence intervals obtained by the three estimation methods may be more significant. For instance, for the 7-day low
flows the ratio between the estimated confidence interval to the estimated quantile based on ML is about 17% for T≥2 while it is about 30% for estimation based on MOM and PWM methods. In addition, the analysis of the rainfall data using
the three-parameter Weibull showed that while ML parameters can be estimated, the corresponding confidence limits and intervals
could not be found because the shape parameter was smaller than 2. |
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