We introduce a novel varying-weight dependent Dirichlet process (DDP) model that extends a recently developed semi-parametric generalized linear model (SPGLM) by adding a nonparametric Bayesian prior on the baseline distribution of the GLM. We show that the resulting model takes the form of an inhomogeneous completely random measure that arises from exponential tilting of a normalized completely random measure. Building on familiar posterior sampling methods for mixtures with respect to normalized random measures, we introduce posterior simulation in the resulting model. We validate the proposed methodology through extensive simulation studies and illustrate its application using data from a speech intelligibility study.

翻译：本文提出了一种新颖的变权重依赖狄利克雷过程（DDP）模型，该模型通过在半参数广义线性模型（SPGLM）的基线分布上添加非参数贝叶斯先验，扩展了近期发展的半参数广义线性模型。我们证明，所得模型呈现为非齐次完全随机测度的形式，该测度源于归一化完全随机测度的指数倾斜。基于对归一化随机测度混合模型已有的成熟后验抽样方法，我们引入了该模型的后验模拟方法。我们通过大量模拟研究验证了所提方法的有效性，并利用语音清晰度研究的数据展示了其实际应用。