Predictive probability as a criterion for model selection

Authors

Petar M. Djuric, University of Rhode Island
Steven M. Kay, University of Rhode Island

Document Type

Conference Proceeding

Date of Original Version

12-1-1990

Abstract

A model selection criterion based on Bayesian predictive densities is derived. Starting with an improper prior distribution of the model parameters and using one portion of the data, a proper distribution is obtained which is further used as a prior for obtaining predictive densities according to the model and the first portion of the data. The remaining portion is used to validate the model through the obtained predictive densities. The procedure is applied to the set of linear regression models. The performance of the criterion is illustrated by simulation results.

Publication Title, e.g., Journal

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volume

5

Citation/Publisher Attribution

Djuric, Petar M., and Steven M. Kay. "Predictive probability as a criterion for model selection." ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings 5, (1990): 2415-2418. doi: 10.1109/ICASSP.1990.116074.