论文标题
偏斜的概率回归 - 可识别性，收缩和重新制定
Skewed probit regression -- Identifiability, contraction and reformulation
论文作者
论文摘要
偏斜的概率回归只是统计模型的一个示例，该模型概括了一个更简单的模型，例如概率回归。所有偏斜的对称分布和链接函数均来自对称分布，通过通过某些偏斜机制结合偏度参数来引起对称分布。在这项工作中，我们解决了偏斜的概率回归中的一些基本问题，并且更巧妙地偏斜了对称分布或偏斜的链接函数。我们通过重新划出第一原则的截距来解决偏斜概率模型参数可识别性的问题。给出了偏斜链接功能的新标准化，以提供推理的解释和锚定解释。研究了可能的偏度参数，并得出了这些惩罚的复杂性先验。此先验是在偏度参数重新聚集的情况下不变的，并量化了偏斜的概率模型对概率模型的收缩。提出的结果可在R-INLA软件包中获得，我们使用模拟数据以及使用链接以及可能性的众所周知的数据集说明了这项工作的使用和效果。
Skewed probit regression is but one example of a statistical model that generalizes a simpler model, like probit regression. All skew-symmetric distributions and link functions arise from symmetric distributions by incorporating a skewness parameter through some skewing mechanism. In this work we address some fundamental issues in skewed probit regression, and more genreally skew-symmetric distributions or skew-symmetric link functions. We address the issue of identifiability of the skewed probit model parameters by reformulating the intercept from first principles. A new standardization of the skew link function is given to provide and anchored interpretation of the inference. Possible skewness parameters are investigated and the penalizing complexity priors of these are derived. This prior is invariant under reparameterization of the skewness parameter and quantifies the contraction of the skewed probit model to the probit model. The proposed results are available in the R-INLA package and we illustrate the use and effects of this work using simulated data, and well-known datasets using the link as well as the likelihood.
