A logistic regression model is a specialized model for product-binomial data. When a proper, noninformative prior is placed on the unrestricted model for the product-binomial model, the hypothesis H 0 of a logistic regression model holding can then be assessed by comparing the concentration of the posterior distribution about H 0 with the concentration of the prior about H 0. This comparison is effected via a relative belief ratio, a measure of the evidence that H 0 is true, together with a measure of the strength of the evidence that H 0 is either true or false. This gives an effective goodness of fit test for logistic regression.

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使用相对信念的逻辑回归模型的拟合优度

Logistic回归模型是用于产品二项式数据的专门模型。当将适当的，非信息性的先验值放在乘积二项式模型的无限制模型上时，可以通过将关于H 0的后验分布的浓度与先验值的浓度进行比较，来评估逻辑回归模型保持的假设H 0。该比较通过相对置信度，H 0为真的证据的度量以及H 0为真或假的证据的强度的度量来实现。这为逻辑回归提供了拟合检验的有效优势。