Nonparametric multiple change point estimation in highly dependent time series

Khaleghi, Azadeh and Ryabko, Daniil (2016) Nonparametric multiple change point estimation in highly dependent time series. Theoretical Computer Science, 620. pp. 119-133. ISSN 0304-3975

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Theoretical Computer Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700
Subjects:
ID Code:
78416
Deposited By:
Deposited On:
09 Aug 2016 15:20
Refereed?:
Yes
Published?:
Published
Last Modified:
17 Feb 2020 12:30