An Expected Utility Approach to Influence Diagnostics
提出一种基于期望效用的影响诊断方法，用于识别参数建模中异常数据子集，适用于线性模型、分层模型和非线性模型。
Abstract We consider the problem of defining the influence of a set of observations in a parametric modeling framework. An expected utility approach, motivated by the amount of information to be gained from an experiment, is developed with regard to the parameter of interest. In some linear model cases simple closed-form expressions for our criterion may be found. In more complicated settings an adaptive Monte Carlo integration technique known as the Gibbs sampler provides a natural framework for evaluating the influence diagnostic. We demonstrate that the influence diagnostic obtained performs well in flagging aberrant subsets of the data, exemplified in the cases of a two-stage linear model, a hierarchical model, and a nonlinear Michaelis-Menten model.