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

Alice Cleynen; Emilie Lebarbier

ESAIM: Probability and Statistics (2014)

- Volume: 18, page 750-769
- ISSN: 1292-8100

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topCleynen, Alice, and Lebarbier, Emilie. "Segmentation of the Poisson and negative binomial rate models: a penalized estimator." ESAIM: Probability and Statistics 18 (2014): 750-769. <http://eudml.org/doc/274386>.

@article{Cleynen2014,

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.},

author = {Cleynen, Alice, Lebarbier, Emilie},

journal = {ESAIM: Probability and Statistics},

keywords = {distribution estimation; change-point detection; count data (RNA-seq); poisson and negative binomial distributions; model selection; Poisson and negative binomial distributions},

language = {eng},

pages = {750-769},

publisher = {EDP-Sciences},

title = {Segmentation of the Poisson and negative binomial rate models: a penalized estimator},

url = {http://eudml.org/doc/274386},

volume = {18},

year = {2014},

}

TY - JOUR

AU - Cleynen, Alice

AU - Lebarbier, Emilie

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

JO - ESAIM: Probability and Statistics

PY - 2014

PB - EDP-Sciences

VL - 18

SP - 750

EP - 769

AB - 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.

LA - eng

KW - distribution estimation; change-point detection; count data (RNA-seq); poisson and negative binomial distributions; model selection; Poisson and negative binomial distributions

UR - http://eudml.org/doc/274386

ER -

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