Some Remarks on Overdispersion
本文证明，在存在适度过度离散时，简单模型的最大似然估计仍保持高效率，主要条件是目标参数应为指数族分布的矩参数或n−1阶偏差为零的参数，并概述了含解释变量模型的扩展。
It is shown that maximum likelihood estimation of a simple model retains high efficiency in the presence of modest amounts of overdispersion. The main requirement is that the target parameter should be the moment parameter of an exponential family distribution, or more generally of a parameter for which the order n−1 bias of the maximum likelihood estimate is zero. Extensions for models with explanatory variables are outlined.