Parametric and Nonparametric Sequential Change Detection in R: The cpm Package

Gordon J. Ross

Journal of Statistical Software, 2015, vol. 066, issue i03

Abstract: The change point model framework introduced in Hawkins, Qiu, and Kang (2003) and Hawkins and Zamba (2005a) provides an effective and computationally efficient method for detecting multiple mean or variance change points in sequences of Gaussian random variables, when no prior information is available regarding the parameters of the distribution in the various segments. It has since been extended in various ways by Hawkins and Deng (2010), Ross, Tasoulis, and Adams (2011), Ross and Adams (2012) to allow for fully nonparametric change detection in non-Gaussian sequences, when no knowledge is available regarding even the distributional form of the sequence. Another extension comes from Ross and Adams (2011) and Ross (2014) which allows change detection in streams of Bernoulli and Exponential random variables respectively, again when the values of the parameters are unknown. This paper describes the R package cpm, which provides a fast implementation of all the above change point models in both batch (Phase I) and sequential (Phase II) settings, where the sequences may contain either a single or multiple change points.

Date: 2015-08-27
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:066:i03

DOI: 10.18637/jss.v066.i03

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