Robust open Bayesian analysis: Overfitting, model uncertainty, and endogeneity issues in multiple regression models
针对多元线性模型的贝叶斯模型平均中的过拟合、模型不确定性、内生性及动态设定错误问题，提出一种基于多先验和迭代MCMC算法的稳健开放贝叶斯方法，并用实证与模拟示例展示其性能。
The paper develops a computational method to deal with some open issues related to Bayesian model averaging for multiple linear models: overfitting, model uncertainty, endogeneity issues, and misspecified dynamics. The methodology takes the name of Robust Open Bayesian procedure. It is robust because the Bayesian inference is performed with a set of priors rather than a single prior and open because the model class is not fully known in advance, but rather is defined iteratively by MCMC algorithm. Conjugate informative priors are used to compute exact posterior probabilities. Empirical and simulated examples describe the functioning and performance of the procedure. Discussions with related works are also accounted for.