24,288 research outputs found
Nonparametric Segment Detection
Algorithms and the Foundations of Software technolog
Nonparametric Edge Detection in Speckled Imagery
We address the issue of edge detection in Synthetic Aperture Radar imagery.
In particular, we propose nonparametric methods for edge detection, and
numerically compare them to an alternative method that has been recently
proposed in the literature. Our results show that some of the proposed methods
display superior results and are computationally simpler than the existing
method. An application to real (not simulated) data is presented and discussed.Comment: Accepted for publication in Mathematics and Computers in Simulatio
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
Segment Parameter Labelling in MCMC Mean-Shift Change Detection
This work addresses the problem of segmentation in time series data with
respect to a statistical parameter of interest in Bayesian models. It is common
to assume that the parameters are distinct within each segment. As such, many
Bayesian change point detection models do not exploit the segment parameter
patterns, which can improve performance. This work proposes a Bayesian
mean-shift change point detection algorithm that makes use of repetition in
segment parameters, by introducing segment class labels that utilise a
Dirichlet process prior. The performance of the proposed approach was assessed
on both synthetic and real world data, highlighting the enhanced performance
when using parameter labelling
ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data
There are many different ways in which change point analysis can be
performed, from purely parametric methods to those that are distribution free.
The ecp package is designed to perform multiple change point analysis while
making as few assumptions as possible. While many other change point methods
are applicable only for univariate data, this R package is suitable for both
univariate and multivariate observations. Estimation can be based upon either a
hierarchical divisive or agglomerative algorithm. Divisive estimation
sequentially identifies change points via a bisection algorithm. The
agglomerative algorithm estimates change point locations by determining an
optimal segmentation. Both approaches are able to detect any type of
distributional change within the data. This provides an advantage over many
existing change point algorithms which are only able to detect changes within
the marginal distributions
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