In the framework of generalized linear models for binary responses, we develop parametric methods that yield estimators for regression coefficients less compromised by an inadequate posited link function. The improved inference are obtained without correcting a misspecified model, and thus are referred to as wrong-model inference. A byproduct of the proposed methods is a simple test for link misspecification in this class of models. Impressive bias reduction in estimators for the regression coefficients from the proposed methods and promising power of the proposed test to detect link misspecification are demonstrated in simulation studies. We also apply these methods to a classic data example frequently analyzed in the existing literature concerning this class of models.

在二元响应的广义线性模型的框架下，我们开发了参数方法，可为因不适当的链接函数造成的影响较小的回归系数提供估计量。在不校正指定不正确的模型的情况下获得了改进的推断，因此将其称为错误模型推断。所提出方法的副产品是在此类模型中链路不合规格的简单测试。在仿真研究中，证明了从所提出的方法得出的回归系数的估计量中的估计偏差令人印象深刻，并且所提出的检测链接错误指定的测试的潜力很有希望。我们还将这些方法应用于经典数据示例中，该示例在现有文献中经常涉及此类模型。