771 research outputs found
Nonparametric maximum likelihood approach to multiple change-point problems
In multiple change-point problems, different data segments often follow
different distributions, for which the changes may occur in the mean, scale or
the entire distribution from one segment to another. Without the need to know
the number of change-points in advance, we propose a nonparametric maximum
likelihood approach to detecting multiple change-points. Our method does not
impose any parametric assumption on the underlying distributions of the data
sequence, which is thus suitable for detection of any changes in the
distributions. The number of change-points is determined by the Bayesian
information criterion and the locations of the change-points can be estimated
via the dynamic programming algorithm and the use of the intrinsic order
structure of the likelihood function. Under some mild conditions, we show that
the new method provides consistent estimation with an optimal rate. We also
suggest a prescreening procedure to exclude most of the irrelevant points prior
to the implementation of the nonparametric likelihood method. Simulation
studies show that the proposed method has satisfactory performance of
identifying multiple change-points in terms of estimation accuracy and
computation time.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1210 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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