DETERMINANTAL POINT PROCESS PRIORS FOR BAYESIAN VARIABLE SELECTION IN LINEAR REGRESSION

作者：Kojima Mutsuki; Komaki Fumiyasu

来源：Statistica Sinica, 2016, 26(1): 97-117.

DOI：10.5705/ss.2014.0161

• Collinearity
• empirical Bayes
• g-prior

We propose discrete determinantal point processes (DPPs) for priors on the model parameter in Bayesian variable selection. By our variable selection method, collinear predictors are less likely to be simultaneously selected due to the repulsion property of discrete DPPs. Three types of DPP priors are proposed. Our method is an empirical Bayes approach, so hyperparameters are estimated by maximizing the marginal likelihood. We show the efficiency of the proposed priors through numerical experiments and applications to collinear datasets.

• 出版日期2016-1