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dc.contributor.authorCassese, Alberto
Zhu, Weixuan
Guindani, Michele
Vannucci, Marina
dc.date.accessioned 2021-12-17T20:08:19Z
dc.date.available 2021-12-17T20:08:19Z
dc.date.issued 2019
dc.identifier.citation Cassese, Alberto, Zhu, Weixuan, Guindani, Michele, et al.. "A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection." Bayesian Analysis, 14, no. 2 (2019) Project Euclid: 553-572. https://doi.org/10.1214/18-BA1116.
dc.identifier.urihttps://hdl.handle.net/1911/111874
dc.description.abstract In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or “normal” behavior. In this manuscript, we consider the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States, and propose a Bayesian nonparametric model selection approach to take into account the spatio-temporal dependence of outbreaks. More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations. We show how the proposed modeling framework performs in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for detecting outbreaks over time, with more recent Markov switching based models, and with spike-and-slab Bayesian nonparametric priors that do not take into account spatio-temporal dependence.
dc.language.iso eng
dc.publisher Project Euclid
dc.rights Creative Commons Attribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection
dc.type Journal article
dc.citation.journalTitle Bayesian Analysis
dc.citation.volumeNumber 14
dc.citation.issueNumber 2
dc.identifier.digital 18-BA1116
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1214/18-BA1116
dc.type.publication publisher version
dc.citation.firstpage 553
dc.citation.lastpage 572


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