We examine and compare the predictive properties of classes of ensemble methods, including the recently developed Bayesian predictive synthesis (BPS). We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o show that a specific form of non-linear BPS (as in McAlinn and West, 2019) produces exact minimax predictive distributions for Kullback-Leibler risk and, under certain conditions, quadratic risk. A finite sample simulation study is presented to illustrate our results .
We analyze the combination of multiple predictive distributions for time series data when all forecasts are misspecified. We show that a specific dynamic form of Bayesian predictive synthesis -- a general and coherent Bayesian framework for ensemble methods -- produces exact minimax predictive densities with regard to Kullback-Leibler loss, providing theoretical support for finite sample predictive performance over existing ensemble methods. A simulation study that highlights this theoretical result is presented, showing that dynamic Bayesian predictive synthesis is superior to other ensemble methods using multiple metrics .
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examine and compare the predictive properties of classes of ensemble methods, including the recently developed Bayesian predictive synthesis (BPS). We develop a novel strategy based on stochastic processes, where the predictive processes are expressed as stochastic differential equations, evaluated using It\^{o
analyze the combination of multiple predictive distributions for time series data when all forecasts are misspecified. We
R
form of non-linear BPS (as in McAlinn and West, 2019)
dynamic form of Bayesian predictive synthesis -- a general and coherent Bayesian framework for ensemble methods --
R
distributions for
densities with regard to
R
risk and, under certain conditions, quadratic risk. A finite sample simulation study is presented to illustrate our results
loss, providing theoretical support for finite sample predictive performance over existing ensemble methods. A simulation study that highlights this theoretical result is presented, showing that dynamic Bayesian predictive synthesis is superior to other ensemble methods using multiple metrics