  Given the output of a data source taking values in a finite alphabet, we wish
to detect 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 detect
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, we establish consistency of the CRECHE estimator under
a related toy model, by establishing a fluid limit and using martingale
arguments.
