Outlier Tests for Logistic Regression: A Conditional Approach
本文提出基于条件精确检验的逻辑回归异常值识别方法，通过枚举所有可能响应计算p值，优于渐近近似方法，并应用于两个实例。
We consider exact conditional methods for identifying outliers in logistic regression data. Tests for a single outlier and multiple outliers are developed assuming a logistic slippage model. The p-values for these tests are determined using an explicit enumeration of all possible responses consistent with the observed value of the sufficient statistic. Justifications are given for preferring this computationally intensive approach to standard methods based on asymptotic approximations. The techniques are applied to two examples.