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Nonparametric multiple change point ...
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Document type :
Article dans une revue scientifique
DOI :
10.1016/j.tcs.2015.10.041
Title :
Nonparametric multiple change point estimation in highly dependent time series
Author(s) :
Khaleghi, Azadeh [Auteur]
Ryabko, Daniil [Auteur]
Sequential Learning [SEQUEL]
Journal title :
Theoretical Computer Science
Pages :
119-133
Publisher :
Elsevier
Publication date :
2016
ISSN :
1879-2294
HAL domain(s) :
Statistiques [stat]/Théorie [stat.TH]
Mathématiques [math]/Statistiques [math.ST]
Informatique [cs]/Apprentissage [cs.LG]
English abstract : [en]
Given a heterogeneous time-series sample, the objective is to find points in time, called change points, where the probability distribution generating the data has changed. The data are assumed to have been generated by ...
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Given a heterogeneous time-series sample, the objective is to find points in time, called change points, where the probability distribution generating the data has changed. The data are assumed to have been generated by arbitrary unknown stationary ergodic distributions. No modelling, independence or mixing assumptions are made. A novel, computationally efficient, nonparametric method is proposed, and is shown to be asymptotically consistent in this general framework. The theoretical results are complemented with experimental evaluations.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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