Sample-based quantile function estimation using entropy and self-determined probability weighted moments
The method of conventional probability-weighted moments (PWMs) and the principle of minimum cross entropy were combined to derive an analytical form of quantile function Pandey, 2001. This method is extended and improved in the present paper by utilizing the concept of self determined probability weighted moments (SD-PWMs). Minimum cross entropy principle and SD-PWMs from sample data were used to evaluate the unknown parameters of the cross entropy quantile function. The performances of the SD-PWM based quantile function were compared with those based on conventional PWMs. The algorithm directly accommodates sample outliers by assigning non-exceedance probabilities accordingly, which is extraordinarily suitable for estimating extreme events from a small sample set. The proposed method would be expected to be used in extreme value and risk analyses of engineering systems.
Jian Deng M.D.Pandey Desheng Gu
Central South University, Changsha, China University of Waterloo, Waterloo, Canada
国际会议
上海
英文
2007-10-18（万方平台首次上网日期，不代表论文的发表时间）