AI 中文总结
研究如何测试观测数据与二元广义线性模型的等价性，核心方法是用最小距离法构建检验统计量，用于协变量均为分类变量的情况，通过渐近近似或自助法算临界值，在真实数据集上应用并经模拟研究有限样本性能。
AI 中文摘要
我们引入一种新的等价性检验，以表明观测数据与二元广义线性模型（GLM）有足够好的一致性。检验统计量通过最小距离法构建，该检验针对所有协变量均为分类变量的重要特殊情况开发。临界值可通过渐近近似或自助法计算。在两个真实数据集上展示了该检验在逻辑回归中的应用，并通过基于这两个数据集的模拟研究了所提检验的有限样本性能。
英文摘要
We introduce a new equivalence test to show sufficiently good agreement of observed data with a binary generalized linear model (GLM). The test statistic is constructed via the minimum distance method. The test is developed for the important special case where all covariates are categorical. The critical values can be calculated using an asymptotic approximation or by means of bootstrapping. The application of the test to logistic regression is illustrated on two real data sets. The finite sample performance of the proposed test is studied by simulations which are based on these two data sets.
Journal refStatistics & Probability Letters, Volume 191, 2022
