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Activity Number: 367
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319935 View Presentation
Title: Bayesian Model Selection in Generalized Linear Model
Author(s): Guiling Shi*
Keywords: nonlocal prior ; Bayesian analysis ; high dimension

In generalized linear model(GLM), variable selection is the problem of interest, especially in clinical statistical research because of high dimension issue. Bayesian methods has many advantages in solving variable selection problem, most existing Bayeisan methods in GLM employ a prior which put a great mass on density at null value. While nonlocal prior could also efficiently eliminate unnecessary covariates, this theory has been developed in linear model. My contribution is extending this nonlocal prior Bayesian method to GLM, also convergence rate is developed under some conditions. Application result showed this method could recognize the model with good prediction performance, and model result is simpler than LASSO in GLM.

Authors who are presenting talks have a * after their name.

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