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

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.