A Normal-Gamma Dirichlet Process Mixture Model

10/22/2020
by   Dawei Ding, et al.
0

We propose a Dirichlet process mixture (DPM) for prediction and cluster-wise variable selection, based on a Normal-Gamma baseline distribution on the linear regression coefficients, and which gives rise to strong posterior consistency. A simulation study and real data application showed that in terms of predictive and variable selection accuracy, the model tended to outperform the standard DPM model assigned a normal prior with no variable selection. Software code is provided in the Supplementary Information.

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