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dc.contributor.authorKowal, Daniel R.
Matteson, David S.
Ruppert, David
dc.date.accessioned 2019-11-14T17:52:17Z
dc.date.available 2019-11-14T17:52:17Z
dc.date.issued 2019
dc.identifier.citation Kowal, Daniel R., Matteson, David S. and Ruppert, David. "Dynamic shrinkage processes." Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81, no. 4 (2019) Wiley: 781-804. https://doi.org/10.1111/rssb.12325.
dc.identifier.urihttps://hdl.handle.net/1911/107676
dc.description.abstract 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.
dc.language.iso eng
dc.publisher Wiley
dc.rights This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.title Dynamic shrinkage processes
dc.type Journal article
dc.citation.journalTitle Journal of the Royal Statistical Society: Series B (Statistical Methodology)
dc.citation.volumeNumber 81
dc.citation.issueNumber 4
dc.identifier.digital Dynamic-shrinkage-processes
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1111/rssb.12325
dc.type.publication publisher version
dc.citation.firstpage 781
dc.citation.lastpage 804


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