Prediction in M-complete Problems with Limited Sample Size

Authors

• Jennifer Lynn Clarke, University of MiamiFollow
• Bertrand Clarke, University of MiamiFollow
• Chi-Wai Yu, Hong Kong University of Science and TechnologyFollow

Document Type

Article

Date of this Version

2013

Citation

2013 International Society for Bayesian Analysis

Comments

Bayesian Analysis (2013) 8, Number 3, pp. 647{690

Abstract

We define a new Bayesian predictor called the posterior weighted median (PWM) and compare its performance to several other predictors including the Bayes model average under squared error loss, the Barbieri-Berger median model predictor, the stacking predictor, and the model average predictor based on Akaike's information criterion. We argue that PWM generally gives better performance than other predictors over a range of M-complete problems. This range is between the M-closed-M-complete boundary and the M-complete- M-open boundary. Indeed, as a problem gets closer to M-open, it seems that M-complete predictive methods begin to break down. Our comparisons rest on extensive simulations and real data examples.