论文标题
Weibull模型形状的原则基于距离的先验
A principled distance-based prior for the shape of the Weibull model
论文作者
论文摘要
由于目的是没有强大的先验信息，因此使用平坦或弱信息的先验者的使用是流行的。在Weibull模型的情况下，不正确的统一，相等的参数伽玛和Jeffrey联合的形状参数先验是流行的选择。这些先验的效果和行为尚未从建模的角度确定，尤其是它们将其减少到更简单的指数模型的能力。在这项工作中，我们提出了Weibull模型的形状参数的新原则，源自距离函数的先验，并在没有强大的先验信息的情况下倡导这一新的先验作为原则上的选择。然后可以将此新的先验用于具有Weibull建模组件的模型，例如竞争风险，关节和空间模型，以提及一些。此先验可在R-INLA中使用，可用于使用，并使用INLA方法应用于关节纵向生存模型框架。
The use of flat or weakly informative priors is popular due to the objective a priori belief in the absence of strong prior information. In the case of the Weibull model the improper uniform, equal parameter gamma and joint Jeffrey's priors for the shape parameter are popular choices. The effects and behaviors of these priors have yet to be established from a modeling viewpoint, especially their ability to reduce to the simpler exponential model. In this work we propose a new principled prior for the shape parameter of the Weibull model, originating from a prior on the distance function, and advocate this new prior as a principled choice in the absence of strong prior information. This new prior can then be used in models with a Weibull modeling component, like competing risks, joint and spatial models, to mention a few. This prior is available in the R-INLA for use, and is applied in a joint longitudinal-survival model framework using the INLA method.
