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June 2019 A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection
Alberto Cassese, Weixuan Zhu, Michele Guindani, Marina Vannucci
Bayesian Anal. 14(2): 553-572 (June 2019). DOI: 10.1214/18-BA1116


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.


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Alberto Cassese. Weixuan Zhu. Michele Guindani. Marina Vannucci. "A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection." Bayesian Anal. 14 (2) 553 - 572, June 2019.


Published: June 2019
First available in Project Euclid: 10 August 2018

zbMATH: 07045442
MathSciNet: MR3934097
Digital Object Identifier: 10.1214/18-BA1116

Keywords: Markov random field , nonparametric Bayes , spatio-temporal data , Variable selection


Vol.14 • No. 2 • June 2019
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