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Activity Number: 512 - Bayesian Model Selection
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #324537 View Presentation
Title: Bayesian Model Selection in the Presence of Heteroscedasticity in Linear Models
Author(s): Thomas Metzger* and Christopher T. Franck
Companies: Virginia Tech and Virginia Tech Department of Statistics
Keywords: Heteroscedasticity ; Bayesian ; Model Selection
Abstract:

The presence of heteroscedasticity poses unique challenges: first, non-constant variance must be accurately detected; and second, usual methods that assume constant variance must be adjusted to account for the more complex variance structure. After a brief review of current approaches, we demonstrate properties and performance of mixture g priors for coefficients under a variety of priors for variances in the heteroscedastic case. We will analyze both simulated and real-world data sets to illustrate the ability to detect heteroscedasticity and estimate parameters using the two-way factorial layout as a case study. Finally, model averaging-based extensions of the selection procedures will be motivated to allow inference on model parameters in the presence of model uncertainty.


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

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