Asymptotic Normmality of Maximum Likelihood Estimators Obtained from Normally Distributed but Dependent Observations
在多参数框架下，假设观测联合正态但非独立非同分布，给出了最大似然估计量一阶有效和渐近正态的直观可验证条件，包含五个定理。
In this article we aim to establish intuitively appealing and verifiable conditions for the first-order efficiency and asymptotic normality of ML estimators in a multi-parameter framework, assuming joint normality but neither the independence nor the identical distribution of the observations. We present five theorems (and a large number of lemmas and propositions), each being a special case of its predecessor.