6 papers
Automated selection of r for stationary and nonstationary models for r largest order statistics
Yire Shin, Jihong Park, Jeong-Soo Park
In generalized extreme value model for the r largest order statistics, denoted by rGEV, the selection of r is critical. The existing entropy difference test for selecting r is appl…
Model averaging with mixed criteria for estimating high quantiles of extreme values: Application to heavy rainfall
Yonggwan Shin, Yire Shin, Jeong-Soo Park
Accurately estimating high quantiles beyond the largest observed value is crucial for risk assessment and devising effective adaptation strategies to prevent a greater disaster. Th…
Generalized method of L-moment estimation for stationary and nonstationary extreme value models
Yonggwan Shin, Yire Shin, Jihong Park +1
Precisely estimating out-of-sample upper quantiles is very important in risk assessment and in engineering practice for structural design to prevent a greater disaster. For this pu…
Building nonstationary extreme value model using L-moments
Yire Shin, Yonggwan Shin, Jeong-Soo Park
The maximum likelihood estimation for a time-dependent nonstationary (NS) extreme value model is often too sensitive to influential observations, such as large values toward the en…
Modeling climate extremes using the four-parameter kappa distribution for -largest order statistics
Yire Shin, Jeong-Soo Park
Accurate estimation of the T-year return levels of climate extremes using statistical distribution is a critical step in the projection of future climate and in engineering design…
Generalized logistic model for largest order statistics, with hydrological application
Yire Shin, Jeong-Soo Park
The effective use of available information in extreme value analysis is critical because extreme values are scarce. Thus, using the largest order statistics (rLOS) instead of t…