HR: 1340h
AN: H43B-1228 [Abstracts]
TI: The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution
AU: * Shin, H
EM: sinong@yonsei.ac.kr
AF: Yonsei University, Shindhon-Dong 134, Seodaemun-Gu, Seoul, 120749, Korea, Republic
of
AU: Heo, J
EM: jhheo@yonsei.ac.kr
AF: Yonsei University, Shindhon-Dong 134, Seodaemun-Gu, Seoul, 120749, Korea, Republic
of
AU: Kim, T
EM: chaucer@yonsei.ac.kr
AF: Yonsei University, Shindhon-Dong 134, Seodaemun-Gu, Seoul, 120749, Korea, Republic
of
AU: Jung, Y
EM: yhjung2000@yonsei.ac.kr
AF: Yonsei University, Shindhon-Dong 134, Seodaemun-Gu, Seoul, 120749, Korea, Republic
of
AB:
The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little
study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution.
In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on
the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the
asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and
parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived
confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error
(RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size
increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of
RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is
more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual
maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML
and PWM while distinct differences in MOM.
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting