Locating changes in highly-dependent data with an unknown number of change-points

Khaleghi, Azedeh and Ryabko, Daniil (2012) Locating changes in highly-dependent data with an unknown number of change-points. In: Advances in Neural Information Processing Systems 25 (NIPS 2012). UNSPECIFIED, USA, pp. 1-9. ISBN 9781627480031

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Abstract

The problem of multiple change point estimation is considered for sequences with unknown number of change points. A consistency framework is suggested that is suitable for highly dependent time-series, and an asymptotically consistent algorithm is proposed. In order for the consistency to be established the only assumption required is that the data is generated by stationary ergodic time-series distributions. No modeling, independence or parametric assumptions are made; the data are allowed to be dependent and the dependence can be of arbitrary form. The theoretical results are complemented with experimental evaluations.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
75765
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Deposited On:
28 Sep 2015 15:30
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Aug 2020 05:22