Mean bounds and existence: Calibration approach via inverse hazard rates
针对解析复杂的连续概率分布，提出一种通过校准分布的逆风险率来逼近原分布的方法，从而得到生存函数和均值效用的上下界，并应用于广义第二类贝塔分布。
This paper provides bounds on the survival function and the mean of an increasing utility function for continuous probability distributions that are analytically complex. The idea is to select a calibration distribution whose inverse hazard rate approximates that of the original under specified criteria. Consequently, the survival function and mean utility of the original distribution fall within the corresponding bounds of the calibration distribution. Moreover, the non-existence of the calibration mean utility implies the same for the original. As an application, we derive the upper bound on the mean utility of the Generalized Beta of the Second Kind (GB2) distribution.