Dynamic shrinkage processes

Dynamic shrinkage processes

We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tr...

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Veröffentlicht in:Journal of the Royal Statistical Society. Series B (Statistical Methodology). - Blackwell Publishers. - 81(2019), 4, Seite 781-804
1. Verfasser: Kowal, Daniel R. (VerfasserIn)
Weitere Verfasser: Matteson, David S., Ruppert, David
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2019
Zugriff auf das übergeordnete Werk:Journal of the Royal Statistical Society. Series B (Statistical Methodology)
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520 |a We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a Pólya–gamma scale mixture representation of the process proposed. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than do competing methods, and we apply the model for irregular curve fitting of minute-by-minute Twitter central processor unit usage data. In addition, we develop an adaptive time varying parameter regression model to assess the efficacy of the Fama–French five-factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that, with the exception of the market risk, no other risk factors are significant except for brief periods. 
540 |a © 2019 The Authors 
655 4 |a research-article 
700 1 |a Matteson, David S.  |e verfasserin  |4 aut 
700 1 |a Ruppert, David  |e verfasserin  |4 aut 
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773 1 8 |g volume:81  |g year:2019  |g number:4  |g pages:781-804 
856 4 0 |u https://www.jstor.org/stable/26773234  |3 Volltext 
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