Segmentation of the Poisson and negative binomial rate models: a penalized estimator
ESAIM: Probability and Statistics, Volume 18 (2014), pp. 750-769.

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.

DOI: 10.1051/ps/2014005
Classification: 62G05,  62G07,  62P10
Keywords: distribution estimation, change-point detection, count data (RNA-seq), poisson and negative binomial distributions, model selection
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author = {Cleynen, Alice and Lebarbier, Emilie},
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Cleynen, Alice; Lebarbier, Emilie. Segmentation of the Poisson and negative binomial rate models: a penalized estimator. ESAIM: Probability and Statistics, Volume 18 (2014), pp. 750-769. doi : 10.1051/ps/2014005. http://www.numdam.org/articles/10.1051/ps/2014005/

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