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2013 Combining predictive distributions
Tilmann Gneiting, Roopesh Ranjan
Electron. J. Statist. 7(none): 1747-1782 (2013). DOI: 10.1214/13-EJS823

Abstract

In probabilistic forecasting combination formulas for the aggregation of predictive distributions need to be estimated based on past experience and training data. We study combination formulas and aggregation methods for predictive cumulative distribution functions from the perspectives of calibration and dispersion, taking an original prediction space approach that applies to discrete, mixed discrete-continuous and continuous predictive distributions alike. The key idea is that aggregation methods ought to be parsimonious, yet sufficiently flexible to accommodate any type of dispersion in the component distributions. Both linear and non-linear aggregation methods are investigated, including generalized, spread-adjusted and beta-transformed linear pools. The effects and techniques are demonstrated theoretically, in simulation examples, and in case studies, where we fit combination formulas for density forecasts of S&P 500 returns and daily maximum temperature at Seattle-Tacoma Airport.

Citation

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Tilmann Gneiting. Roopesh Ranjan. "Combining predictive distributions." Electron. J. Statist. 7 1747 - 1782, 2013. https://doi.org/10.1214/13-EJS823

Information

Published: 2013
First available in Project Euclid: 3 July 2013

zbMATH: 1294.62220
MathSciNet: MR3080409
Digital Object Identifier: 10.1214/13-EJS823

Subjects:
Primary: 62
Secondary: 91B06

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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