Discussion: Performance of Bayesian Model Averaging

By Adrian E. Raftery, Yingye Zheng. 2003

218 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e31e67fd5ffd8c6af1fd46640ecf05092f1f5c16

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In Global and Planetary Change

In Statistics and Probability

Citations in the corpus

INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, unknown): “However, there has been a discussion about the realism of the local asymptotic framework; see Hjort and Claeskens (2003b) and Raftery and Zheng (2003).”
A survey of Bayesian predictive methods for model assessment, selection and comparison (2012, background): “Although it is difficult to find situations in which the strict interpretation of the M-closed view holds, Bayesian model averaging has been shown to have good predictive performance (Raftery and Zheng, 2003).”
Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, background): “BMA is a standard approach to inference in the presence of multiple competing models (Raftery and Zheng, 2003).”
Bayesian and LASSO Regressions for Comparative Permeability Modeling of Sandstone Reservoirs (2018, methodology): “The BMA takes into account models uncertainty that might come from the variable selection problem by averaging over the best models according to approximate posterior model probability (Kass and Raftery 1995; Viallefont et al. 2001; Raftery and Zheng 2003; Al-Mudhafar 2015).”
Comparison of Bayesian predictive methods for model selection (2015, background): “This result is in perfect accordance with what is known about the good performance of the BMA (Hoeting et al., 1999; Raftery and Zheng, 2003).”
MMI: Multimodel inference or models with management implications? (2015, methodology): “In particular, model averaging has been shown in many cases to decrease prediction error, relative to any single model (e.g., Hoeting et al. 1999, Hjort and Claeskens 2003, Raftery and Zheng 2003).”
Using Bayesian Model Averaging to Calibrate Forecast Ensembles (2005, background): “BMA possesses a range of theoretical optimality properties and has shown good performance in a variety of simulated and real data situations (Raftery and Zheng 2003).”
Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging (2007, unknown): “…more accurate and reliable predictions than other available multimodel techniques [George and McCulloch, 1993; Raftery et al., 1997; Clyde, 1999; Raftery and Zheng, 2003; Ye et al., 2004; Ajami et al., 2005, 2007; Q. Duan et al., Multi-model ensemble hydrologic prediction using Bayesian model…”
Model Averaging and Its Use in Economics (2017, background): “• BMA benefits from a number of appealing statistical properties, such as point estimators and predictors that minimize prior-weighted Mean Squared Error (MSE), and the calibration of the associated intervals (Raftery and Zheng, 2003).”
Combining forecast densities from VARs with uncertain instabilities (2010, methodology): “…density combination based on recursive logarithmic score weights, RW, has many similarities with an approximate predictive likelihood approach (see Raftery & Zheng (2003), and Eklund & Karlsson (2007)).4 Given our definition of density fit, the model densities are combined using Bayes’ rule with…”
Multi-model ensemble hydrologic prediction using Bayesian model averaging (2007, unknown): “, 1997; [8,40,31,30,14].”
An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction (2006, methodology): “In many case studies, the BMA has shown to produce more accurate and reliable predictions than other multimodel techniques [George and McCulloch, 1993; Raftery et al., 1997; Clyde, 1999; Viallefont et al., 2001; Raftery and Zheng, 2003; Ellison, 2004].”