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Segmentation of the Poisson and negative binomial rate models: a penalized estimator

Published online by Cambridge University Press:  22 October 2014

Alice Cleynen  and
Emilie Lebarbier
Alice Cleynen
Affiliation:
AgroParisTech UMR518, Paris 5e, France. alice.cleynen@agroparistech.fr
Emilie Lebarbier
Affiliation:
INRA UMR518, Paris 5e, France; emilie.lebarbier@agroparistech.fr
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Abstract

We consider the segmentation problem of Poisson and negative binomial (i.e. overdispersed Poisson) rate distributions. In segmentation, an important issue remains the choice of the number of segments. To this end, we propose a penalized -likelihood estimator where the penalty function is constructed in a non-asymptotic context following the works of L. Birgé and P. Massart. The resulting estimator is proved to satisfy an oracle inequality. The performances of our criterion is assessed using simulated and real datasets in the RNA-seq data analysis context.

Keywords

Distribution estimationchange-point detectioncount data (RNA-seq)poisson and negative binomial distributionsmodel selection
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 18 , 2014 , pp. 750 - 769
Copyright
© EDP Sciences, SMAI 2014

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