1 |
Busababodhin P, Seo YA, Park JS, and Kumphon B (2016). LH-moment estimation ofWakeby distribution with hydrological applications. Stochastic Environmental Research and Risk Assessment, 30, 1757-1767.
DOI
|
2 |
Castillo E, Hadi AS, Balakrishnan N, and Sarabia JM (2005). Extreme Value and Related Models with Applications in Engineering and Science, Wiley-Interscience, New Jersey.
|
3 |
Choi H (2015). A note on the dependence conditions for stationary normal sequences. Communications for Statistical Applications and Methods, 22, 647-653.
DOI
|
4 |
Coles S (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, New York.
|
5 |
Coles SG and Dixon MJ (1999). Likelihood-based inference for extreme value models. Extremes, 2, 5-23.
|
6 |
Efron B and Tibshirani RJ (1993). An Introduction to the Bootstrap, Chapman & Hall/CRC, Boca Raton.
|
7 |
Hosking JRM (1990). L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society Series B (Methodological), 52, 105-124.
|
8 |
Hosking JRM andWallis JR (1997). Regional Frequency Analysis: An Approach based on L-Moments, Cambridge University Press, Cambridge.
|
9 |
Hosking JRM, Wallis JR, and Wood EF (1985). Estimation of the generalized extreme-value distribution by the method of probability weighted moments. Technometrics, 27, 251-261.
DOI
|
10 |
Huard D, Mailhot A, and Duchesne S (2010). Bayesian estimation of intensity-duration-frequency curves and of the return period associated to a given rainfall event. Stochastic Environmental Research and Risk Assessment, 24, 337-347.
DOI
|
11 |
Katz RW, Parlange MB, and Naveau P (2002). Statistics of extremes in hydrology. Advances in Water Resources, 25, 1287-1304.
DOI
|
12 |
Korea Meteorological Administration (2016). Annual daily maximum precipitation record for 75 weather stations, Retrieved May 1, 2017, from: http://www.kma.go.kr
|
13 |
Martins ES and Stedinger JR (2000). Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resources Research, 36, 737-744.
DOI
|
14 |
Meshgi A and Khalili D (2009). Comprehensive evaluation of regional flood frequency analysis by L- and LH-moments. I. A re-visit to regional homogeneity. Stochastic Environmental Research and Risk Assessment, 23, 119-135.
DOI
|
15 |
Mudholkar GS and Hutson AD (1998). LQ-moments: analogs of L-moments. Journal of Statistical Planning and Inference, 71, 191-208.
DOI
|
16 |
Murshed MS, Seo YA, and Park JS (2014). LH-moment estimation of a four parameter kappa distribution with hydrologic applications. Stochastic Environmental Research and Risk Assessment, 28, 253-262.
DOI
|
17 |
Hosking JRM (2015). Package 'lmom', version 2.5, Retrieved June 9, 2016, from: https://cran.rproject.org/web/packages/lmom/lmom.pdf
|
18 |
Park JS (2005). A simulation-based hyperparameter selection for quantile estimation of the generalized extreme value distribution. Mathematics and Computers in Simulation, 70, 227-234.
DOI
|
19 |
Pericchi LR and Rodriguez-Iturbe I (1985). On the statistical analysis of floods. In Atkinson AC, Fienberg SE (Eds), A Celebration of Statistics (pp. 511-541), Springer, New York.
|
20 |
Yoon S, ChoW, Heo JH, and Kim CE (2010). A full Bayesian approach to generalized maximum like-lihood estimation of generalized extreme value distribution. Stochastic Environmental Research and Risk Assessment, 24, 761-770.
DOI
|
21 |
Zhu J, Forsee W, Schumer R, and Gautam M (2013). Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models. Climatic Change, 118, 469-485.
DOI
|