Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes
Abstract
This work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.
Citation
Altieri , L , Cocchi , D , Greco , F , Illian , J B & Scott , E M 2016 , ' Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes ' , Journal of Statistical Computation and Simulation , vol. 86 , no. 13 , pp. 2531-2545 . https://doi.org/10.1080/00949655.2016.1146280
Publication
Journal of Statistical Computation and Simulation
Status
Peer reviewed
ISSN
0094-9655Type
Journal article
Rights
© 2016 Informa UK Limited, trading as Taylor & Francis Group. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1080/00949655.2016.1146280
Description
As regards authors Linda Altieri and Fedele Greco, the research work underlying this paper was partially funded by an FIRB 2012 [grant number RBFR12URQJ]; title: Statistical modelling of environmental phenomena: pollution, meteorology, health and their interactions) for research projects by the Italian Ministry of Education, Universities and Research.Collections
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