Goodness–of–fit tests based on the min–characteristic function
提出一种基于最小特征函数的拟合优度检验方法，适用于威布尔、帕累托和弗雷歇分布族，通过L2加权距离比较理论函数与经验函数，蒙特卡洛模拟显示其有效性。
Tests of fit for classes of distributions that include the Weibull, the Pareto and the Fréchet families are proposed. The new tests employ the novel tool of the min–characteristic function and are based on an L2–type weighted distance between this function and its empirical counterpart applied on suitably standardized data. If data–standardization is performed using the MLE of the distributional parameters then the method reduces to testing for the standard member of the family, with parameter values known and set equal to one. Asymptotic properties of the tests are investigated. A Monte Carlo study is presented that includes the new procedure as well as competitors for the purpose of specification testing with three extreme value distributions. The new tests are also applied on a few real–data sets.