Nonparametric multiple change point estimation in highly dependent time series

Khaleghi, Azedeh and Ryabko, Daniil (2013) Nonparametric multiple change point estimation in highly dependent time series. In: Algorithmic Learning Theory. Lecture Notes in Computer Science . Springer, SGP, pp. 382-396. ISBN 9783642409349

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Abstract

Given a heterogeneous time-series sample, it is required to find the points in time (called change points) where the probability distribution generating the data has changed. The data is 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.

Item Type: Contribution in Book/Report/Proceedings
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 75766
Deposited By: ep_importer_pure
Deposited On: 28 Sep 2015 15:14
Refereed?: Yes
Published?: Published
Last Modified: 15 Jan 2020 06:52
URI: https://eprints.lancs.ac.uk/id/eprint/75766

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