Title :
Non-parametrick Synthesis of Private Probablistic Predictions
Author_Institution :
Dept. of Health Adm. & Policy, George Mason Univ., Fairfax, VA, USA
Abstract :
This paper describes a new non-parameteric method to synthesize probabilistic predictions form different experts. In contrast to the popular linear pooling method that combines forecasts with the weights that reflect the average performance of individual experts over the entire forecast space, our method exploits the information that is local to each prediction case. A simulation study, replicated from [1], shows that our synthesized forecast is calibrated and whose Brier score is close to the theoretically optimal Brier score. Our robust non-parametric algorithm delivers an excellent performance comparable to the best combination method with parametric recalibration - Ranjan-Gneiting´s beta-transformed linear pooling.
Keywords :
data handling; forecasting theory; prediction theory; probability; Ranjan-Gneiting beta-transformed linear pooling; forecast space; nonparametric synthesis; optimal Brier score; parametric recalibration; private probabilistic predictions; probabilistic prediction synthesis; robust nonparametric algorithm; Conferences; Data engineering;
Conference_Titel :
Data Engineering Workshops (ICDEW), 2012 IEEE 28th International Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
978-1-4673-1640-8
DOI :
10.1109/ICDEW.2012.59
