New Ways to Prove Central Limit Theorems
介绍通过最大化随机准则函数定义的统计量的渐近正态性证明技术，结合经验过程理论和Huber方法，适用于非标准条件下的最大似然估计研究。
This paper describes some techniques for proving asymptotic normality of statistics defined by maximization of random criterion function. The techniques are based on a combination of recent results from the theory of empirical processes and a method of Huber for the study of maximum likelihood estimators under nonstandard conditions.