A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection
| dc.citation.firstpage | 553 | en_US |
| dc.citation.issueNumber | 2 | en_US |
| dc.citation.journalTitle | Bayesian Analysis | en_US |
| dc.citation.lastpage | 572 | en_US |
| dc.citation.volumeNumber | 14 | en_US |
| dc.contributor.author | Cassese, Alberto | en_US |
| dc.contributor.author | Zhu, Weixuan | en_US |
| dc.contributor.author | Guindani, Michele | en_US |
| dc.contributor.author | Vannucci, Marina | en_US |
| dc.date.accessioned | 2021-12-17T20:08:19Z | en_US |
| dc.date.available | 2021-12-17T20:08:19Z | en_US |
| dc.date.issued | 2019 | en_US |
| 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. | en_US |
| dc.identifier.citation | Cassese, Alberto, Zhu, Weixuan, Guindani, Michele, et al.. "A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection." <i>Bayesian Analysis,</i> 14, no. 2 (2019) Project Euclid: 553-572. https://doi.org/10.1214/18-BA1116. | en_US |
| dc.identifier.digital | 18-BA1116 | en_US |
| dc.identifier.doi | https://doi.org/10.1214/18-BA1116 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/111874 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Project Euclid | en_US |
| dc.rights | Creative Commons Attribution 4.0 International License | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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