Statistics and Computing

, Volume 22, Issue 2, pp 579–595

On-line changepoint detection and parameter estimation with application to genomic data


DOI: 10.1007/s11222-011-9248-x

Cite this article as:
Caron, F., Doucet, A. & Gottardo, R. Stat Comput (2012) 22: 579. doi:10.1007/s11222-011-9248-x


An efficient on-line changepoint detection algorithm for an important class of Bayesian product partition models has been recently proposed by Fearnhead and Liu (in J. R. Stat. Soc. B 69, 589–605, 2007). However a severe limitation of this algorithm is that it requires the knowledge of the static parameters of the model to infer the number of changepoints and their locations. We propose here an extension of this algorithm which allows us to estimate jointly on-line these static parameters using a recursive maximum likelihood estimation strategy. This particle filter type algorithm has a computational complexity which scales linearly both in the number of data and the number of particles. We demonstrate our methodology on a synthetic and two real-world datasets from RNA transcript analysis. On simulated data, it is shown that our approach outperforms standard techniques used in this context and hence has the potential to detect novel RNA transcripts.


Sequential Monte CarloParticle filteringChangepoint modelsProduct partition modelsRecursive parameter estimationTiling arrays

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • François Caron
    • 1
  • Arnaud Doucet
    • 2
  • Raphael Gottardo
    • 3
  1. 1.INRIA Bordeaux Sud-Ouest and Institut de Matématiques de BordeauxUniversité de BordeauxTalenceFrance
  2. 2.Departments of Statistics & Computer ScienceUniversity of British ColumbiaVancouverCanada
  3. 3.Vaccine and Infectious Disease and Public Health Sciences DivisionsFred Hutchinson Cancer Research CenterSeattleUSA