Response functions linking regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these functions is typically based on the domain of the modeled quantities and is not further scrutinized. For example, the exponential response function is usually assumed for parameters restricted to be positive although it implies a multiplicative model which may not necessarily be desired. Consequently, applied researchers might easily face misleading results when relying on defaults without further investigation. As an alternative to the exponential response function, we propose the use of the softplus function to construct alternative link functions for parameters restricted to be positive. As a major advantage, we can construct differentiable link functions corresponding closely to the identity function for positive values of the regression predictor, which implies an quasi-additive model and thus allows for an additive interpretation of the estimated effects by practitioners. We demonstrate the applicability of the softplus response function using both simulations and real data. In four applications featuring count data regression and Bayesian distributional regression, we contrast our approach to the commonly used exponential response function.
翻译:将回归预测器与响应分布特性相联系的响应功能是许多统计模型的基本组成部分。然而,这些功能的选择通常以模型数量的范围为基础,没有经过进一步仔细审查。例如,指数响应功能通常被假定为限于正数的参数,尽管它意味着一个不一定可取的多倍模型。因此,应用的研究人员在不作进一步调查的情况下依赖默认时很容易面临误导结果。作为指数反应函数的替代,我们提议使用软增函数来为限制为正数的参数构建替代链接功能。作为一个主要优势,我们可以构建与回归预测器正数的特性功能密切对应的不同链接功能,这意味着一个准扩展模型,从而允许对从业人员的估计效果进行添加解释。我们用模拟和真实数据来证明软增响应功能的可适用性。在四个显示数据回归和巴伊西亚分布回归的应用程序中,我们用的方法与常用的指数响应功能作对比。