We propose new parametric frameworks of regression analysis with the conditional mode of a bounded response as the focal point of interest. Covariate effects estimation and prediction based on the maximum likelihood method under two new classes of regression models are demonstrated. We also develop graphical and numerical diagnostic tools to detect various sources of model misspecification. Predictions based on different central tendency measures inferred using various regression models are compared using synthetic data in simulations. Finally, we conduct regression analysis for data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate practical implementation of the proposed methods. Supporting Information that contain technical details and additional simulation and data analysis results are available online.

中文翻译：

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有界响应的参数模式回归

我们提出了新的回归分析参数框架，将有界响应的条件模式作为感兴趣的焦点。证明了在两类新的回归模型下基于最大似然法的协变量效应估计和预测。我们还开发了图形和数字诊断工具来检测模型错误指定的各种来源。在模拟中使用合成数据比较基于使用各种回归模型推断的不同集中趋势度量的预测。最后，我们对来自阿尔茨海默病神经影像学计划的数据进行回归分析，以证明所提出方法的实际实施。包含技术细节和其他模拟和数据分析结果的支持信息可在线获取。