标题： 线性回归模型中变量选择的模型先验分布 显示英文标题

标题： Model Prior Distribution for Variable Selection in Linear Regression Models

摘要： 在本工作中，我们讨论了线性回归中变量选择的一种新模型先验概率。 想法是通过考虑所考虑的协变量数量下每个可能的回归模型的价值，以客观的方式确定先验质量。 通过一个模拟研究，我们表明在对真实回归模型大小没有任何先验知识的情况下，所提出的先验优于均匀先验和 Scott & Berger 先验。 我们使用两个著名的数据集来说明该先验的使用，分别具有 15 和 4 个协变量。 显示英文摘要

摘要： In this work we discuss a novel model prior probability for variable selection in linear regression. The idea is to determine the prior mass in an objective sense, by considering the worth of each of the possible regression models, given the number of covariates under consideration. Through a simulation study, we show that the proposed prior outperforms the uniform prior and the Scott \& Berger prior in a scenario of no prior knowledge about the size of the true regression models. We illustrate the use of the prior using two well-known data sets with, respectively, 15 and 4 covariates.