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Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes

Research output: Contribution to journalArticle


L. Altieri, D. Cocchi, F. Greco, J. B. Illian, E. M. Scott

School/Research organisations


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.



Original languageEnglish
Pages (from-to)2531-2545
Number of pages15
JournalJournal of Statistical Computation and Simulation
Issue number13
Early online date18 Feb 2016
Publication statusPublished - 2016

    Research areas

  • Earthquake data, Changepoint analysis, Spatio-temporal point processes, Spatial effect, Log-Gaussian Cox processes, Bayesian P-splines, Parallel computing, 62H11, 62M30

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