Inference in Generalized Linear Models with Robustness to Misspecified Variances
针对广义线性模型常假设共同离散参数而实际不成立的问题，提出一种基于分数符号翻转的半参数群不变性方法，只需均值模型正确即可稳健处理方差误设，适用于小样本，并已在R包flipscores中实现。
Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer appreciable loss of type I error control. As an alternative, we present a semi-parametric group-invariance method based on sign flipping of score contributions. Our method requires only the correct specification of the mean model, but is robust against any misspecification of the variance. We present tests for single as well as multiple regression coefficients. The test is asymptotically valid but shows excellent performance in small samples. We illustrate the method using RNA sequencing count data, for which it is difficult to model the overdispersion correctly. The method is available in the R library flipscores.