Fast estimation of the Integrated Completed Likelihood criterion for change-point detection problems with applications to Next-Generation Sequencing data

• We propose a estimator of the ICL criteria in the segmentation context.
• It is computed with a linear complexity and can accommodate any distribution.
• We show its good performance compared to the exact version of the ICL.
• We show its encouraging results on long count datasets.

In this paper, we consider the Integrated Completed Likelihood (ICL) as a useful criterion for estimating the number of changes in the underlying distribution of data, specifically in problems where detecting the precise location of these changes is the main goal. The exact computation of the ICL requires O(Kn2)O(Kn2) operations (with K the number of segments and n   the number of data-points) which is prohibitive in many practical situations with large sequences of data. We describe a framework to estimate the ICL with O(K2n)O(K2n) complexity. Our approach is general in the sense that it can accommodate any given model distribution. We checked the run-time and validity of our approach on simulated data and demonstrate its good performance when analyzing real Next-Generation Sequencing (NGS) data using a negative binomial model. Our method is implemented in the R package postCP and available on the CRAN repository.