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

Francois Caron 1, 2 Arnaud Doucet 3 Raphael Gottardo 4
1 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
2 IMB - Institut de Mathématiques de Bordeaux
3 Dept of Statistics & Dept of Computer Science
4 FHCRC - Fred Hutchinson Cancer Research Center [Seattle]
Abstract : 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.
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Article dans une revue
Statistics and Computing, Springer Verlag (Germany), 2012, 22 (2), pp.579-595. 〈10.1007/s11222-011-9248-x〉
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https://hal.inria.fr/inria-00577217
Contributeur : Francois Caron <>
Soumis le : mercredi 16 mars 2011 - 17:35:35
Dernière modification le : vendredi 23 février 2018 - 17:12:11

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Francois Caron, Arnaud Doucet, Raphael Gottardo. On-line changepoint detection and parameter estimation with application to genomic data. Statistics and Computing, Springer Verlag (Germany), 2012, 22 (2), pp.579-595. 〈10.1007/s11222-011-9248-x〉. 〈inria-00577217〉

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