论文标题
一致的贝叶斯信息标准基于先验的混合物，可能是高维多元线性回归模型
Consistent Bayesian Information Criterion Based on a Mixture Prior for Possibly High-Dimensional Multivariate Linear Regression Models
论文作者
论文摘要
在多元线性回归模型中选择变量的问题中，我们基于先前的混合平滑分布和三角洲分布来得出新的贝叶斯信息标准。它们每个人都可以解释为Akaike信息标准（AIC）和贝叶斯信息标准（BIC）的融合。遗传了它们的渐近特性，我们的信息标准在大样本和高渐近渐近框架中的可变选择中均保持一致。在数值模拟中，基于我们的信息标准的变量选择方法在大多数情况下选择具有很高概率的真实变量集。
In the problem of selecting variables in a multivariate linear regression model, we derive new Bayesian information criteria based on a prior mixing a smooth distribution and a delta distribution. Each of them can be interpreted as a fusion of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Inheriting their asymptotic properties, our information criteria are consistent in variable selection in both the large-sample and the high-dimensional asymptotic frameworks. In numerical simulations, variable selection methods based on our information criteria choose the true set of variables with high probability in most cases.
