Bayesian Binary Segmentation Procedure for a Poisson Process With Multiple Changepoints
- 1 December 2001
- journal article
- Published by Taylor & Francis in Journal of Computational and Graphical Statistics
- Vol. 10 (4) , 772-785
- https://doi.org/10.1198/106186001317243449
Abstract
We observe n events occurring in (0, T] taken from a Poisson process. The intensity function of the process is assumed to be a step function with multiple changepoints. This article proposes a Bayesian binary segmentation procedure for locating the changepoints and the associated heights of the intensity function. We conduct a sequence of nested hypothesis tests using the Bayes factor or the BIC approximation to the Bayes factor. At each comparison in the binary segmentation steps, we need only to compare a singlechangepoint model to a no-changepoint model. Therefore, this method circumvents the computational complexity we would normally face in problems with an unknown (large) number of dimensions. A simulation study and an analysis on a real dataset are given to illustrate our methods.Keywords
This publication has 9 references indexed in Scilit:
- Estimation and comparison of multiple change-point modelsPublished by Elsevier ,1999
- Testing and Locating Variance Changepoints with Application to Stock PricesJournal of the American Statistical Association, 1997
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determinationBiometrika, 1995
- Hierarchical Bayesian Analysis of Changepoint ProblemsJournal of the Royal Statistical Society Series C: Applied Statistics, 1992
- Constant hazard against a change-point alternative: a bayesian approach with censored dataCommunications in Statistics - Theory and Methods, 1989
- Bayesian analysis of a Poisson process with a change-pointBiometrika, 1986
- A note on the intervals between coal-mining disastersBiometrika, 1979
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978
- THE TIME INTERVALS BETWEEN INDUSTRIAL ACCIDENTSBiometrika, 1952