Detecting change-points in a discrete distribution via model selection
| dc.creator | Akakpo, Nathalie | |
| dc.date | 2008-01-07 | |
| dc.date.accessioned | 2026-07-07T08:52:57Z | |
| dc.date.available | 2026-07-07T08:52:57Z | |
| dc.description | This paper is concerned with the detection of multiple change-points in the joint distribution of independent categorical variables. The procedures introduced rely on model selection and are based on a penalized least-squares criterion. Their performance is assessed from a nonasymptotic point of view. Using a special collection of models, a preliminary estimator is built. According to an existing model selection theorem, it satisfies an oracle-type inequality. Moreover, thanks to an approximation result demonstrated in this paper, it is also proved to be adaptive in the minimax sense. In order to eliminate some irrelevant change-points selected by that first estimator, a two-stage procedure is proposed, that also enjoys some adaptivity property. Besides, the first estimator can be computed with a complexity only linear in the size of the data. A heuristic method allows to implement the second procedure quite satisfactorily with the same computational complexity. | |
| dc.description | Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org) | |
| dc.identifier | https://arxiv.org/abs/0801.0970 | |
| dc.identifier | http://arxiv.org/abs/0801.0970 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/145453 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62G05, 62C20 (Primary) 41A17 (Secondary) | |
| dc.title | Detecting change-points in a discrete distribution via model selection | |
| dc.type | text |