ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data

By Nicholas A. James, David S. Matteson. 2013

306 citations recorded by the source corpus.

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. Hierarchical estimation can be based upon either a 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.

Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9ed3acccde2bd6f81ead432d1e11bf061656eb48

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Citations in the corpus

Change-point detection in high-dimensional covariance structure (2016, methodology): “uzun/Change-point detection in high-dimensional covariance structure13 test as proposed by the authors. In our experiments the method was allowed to consider all the sample at once. The R-package ecp [25] was used. In [12] ahigh-dimensionalapproachaimingtodetect achange-pointin secondorder structure”
A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data (2013, methodology): “Simulation Simulation Study: Rand Index Compare E-Divisive with a generalized Wilcoxon/MannWhitney approach: the MultiRank procedure Lung-Yut-Fong et al. (2011) For two partitions U & V , the Rand Index considers all pairs of observations: Define {A} Pairs in same cluster under U and in same cluster”
A review of change point detection methods (2018, methodology): “r-project. org/package= not Mean-shifts in univariate signals (with di˛erent types of noise) BinSeg factorcpt [37] R cran.r-project. org/package= factorcpt Mean-shifts with factor analysis BinSeg ecp [71] R cran.r-project. org/package= ecp Distribution changes BinSeg, BotUp changepoint [80] R cran.r”