The Cumulant Generating Function Estimation Method
提出利用经验累积量生成函数对独立同分布数据的参数进行一致估计，适用于密度未知或参数空间无界的情况，并给出六步实施步骤。
This paper deals with the use of the empirical cumulant generating function to consistently estimate the parameters of a distribution from data that are independent and identically distributed (i.i.d.). The technique is particularly suited to situations where the density function is unknown or unbounded in parameter space. We prove asymptotic equivalence of our technique to that of the empirical characteristic function and outline a six-step procedure for its implementation. Extensions of the approach to non-i.i.d. situations are considered along with a discussion of suitable applications and a worked example.