Approximate Conditional Inference in Generalized Linear Models
针对具有规范链接函数的广义线性模型，开发了易于计算且精确的充分统计量条件密度和分布的近似方法，仅需偏差和拟合模型的方差矩阵估计即可进行条件推断，并应用于二元逻辑回归和对数线性模型的模型充分性检验。
SUMMARY Easily calculated and accurate approximations are developed to the conditional densities and distributions of sufficient statistics in generalized linear models with canonical link functions. They enable conditional inferences based on modified profile log-likelihoods and tail probabilities to be made using only the deviance and the variance matrix estimate based on fitted models. Examples are given in binary logistic regression and a log-linear model, and the results are applied to added variable tests of model adequacy.