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Nonparametric multiple change point estimation in highly dependent time series

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>21/03/2016
<mark>Journal</mark>Theoretical Computer Science
Number of pages15
Pages (from-to)119-133
Publication StatusPublished
Early online date11/11/15
<mark>Original language</mark>English


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.