Non-Parametric Change-Point Estimation using String Matching Algorithms

Oliver Johnson (), Dino Sejdinovic, James Cruise, Robert Piechocki and Ayalvadi Ganesh
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Oliver Johnson: University of Bristol
Dino Sejdinovic: University College London
James Cruise: Heriot-Watt University Edinburgh Campus
Robert Piechocki: University of Bristol
Ayalvadi Ganesh: University of Bristol

Methodology and Computing in Applied Probability, 2014, vol. 16, issue 4, 987-1008

Abstract: Abstract Given the output of a data source taking values in a finite alphabet, we wish to estimate change-points, that is times when the statistical properties of the source change. Motivated by ideas of match lengths in information theory, we introduce a novel non-parametric estimator which we call CRECHE (CRossings Enumeration CHange Estimator). We present simulation evidence that this estimator performs well, both for simulated sources and for real data formed by concatenating text sources. For example, we show that we can accurately estimate the point at which a source changes from a Markov chain to an IID source with the same stationary distribution. Our estimator requires no assumptions about the form of the source distribution, and avoids the need to estimate its probabilities. Further, establishing a fluid limit and using martingale arguments.

Keywords: Change-point estimation; Entropy; Non-parametric; String matching; Primary 62L10; Secondary 62M09; 68W32 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11009-013-9359-2

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