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

arXiv: https://arxiv.org/abs/1301.2534 · HTML: https://mlsanity.com/paper/1301.2534/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2013.

## Built on by (most cited first, 1 of 1)

- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)

## Abstract (arXiv)

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 log-likelihood estimator where the penalty function is constructed in a non-asymptotic context following the works of L. Birg'e 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.