Generalized Linear Models-The Missing Link
本文在多元指数族模型框架下重新审视广义线性模型，指出连接函数并非迭代加权最小二乘算法的必要特征，但在有自然线性模型背景时仍有用；通过多项回归、生物测定等例子说明，无单一连接函数时采用指数族模型更优。
We consider generalized linear models, including an extension due to Thompson and Baker (1981), within the larger framework of multiparameter exponential family models. This general approach shows that the link function is not a necessary feature of a computer algorithm for calculating maximum likelihood estimates for such models by iteratively reweighted least squares. It is argued that the link function is a useful component of model fitting and interpretation in situations where there is a natural link to an underlying linear model (e.g., logistic regression). However in many instances there is no single link function (e.g., multinomial regression) or else unlinked parameters exist (e.g., bioassay with a spontaneous response rate). We attempt to show by a number of examples that a general approach via exponential family models is preferable in such situations.