Using the Bayesian Shtarkov solution for predictions

Tri Le and Bertrand Clarke

Computational Statistics & Data Analysis, 2016, vol. 104, issue C, 183-196

Abstract: The Bayes Shtarkov predictor can be defined and used for a variety of data sets that are exceedingly hard if not impossible to model in any detailed fashion. Indeed, this is the setting in which the derivation of the Shtarkov solution is most compelling. The computations show that anytime the numerical approximation to the Shtarkov solution is ‘reasonable’, it is better in terms of predictive error than a variety of other general predictive procedures. These include two forms of additive model as well as bagging or stacking with support vector machines, Nadaraya–Watson estimators, or draws from a Gaussian Process Prior.

Keywords: Bayes; Prequential; Model average; Stacking; Shtarkov predictor; Bagging (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:104:y:2016:i:c:p:183-196

DOI: 10.1016/j.csda.2016.06.018

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