贝叶斯变量选择及其在经济大数据中的应用兼谈美国高校研究生招生 - 汪敏 (密西根理工大学)

In this talk, we consider Bayesian variable selection in linear regression models with related predictors. We propose a generalized singular g-prior for the unknown parameters, which results in a closed-form expression of the marginal posterior distribution. A special prior on the model space is adopted to reflect and maintain the hierarchical relationships among predictors. It is shown that the proposed approach is consistent in terms of model selection and prediction. Simulation studies and real data application are considered for illustrative purposes.

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