Parametric and Nonparametric Sequential Change Detection in R: The cpm Package
Parametric and Nonparametric Sequential Change Detection in R: The cpm Package
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R 中的参数和非参数顺序变化检测：cpm 包
DOI：
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发表时间：
2015
期刊：
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通讯作者：
Gordon J. Ross
中科院分区：
文献类型：
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作者：
Gordon J. Ross
在Hawkins，Qiu和Kang(2003)和Hawkins和Zamba(2005a)中引入的变点模型框架提供了一种有效且计算高效的方法，用于在没有关于各个分段中的分布参数的先验信息的情况下检测高斯随机变量序列中的多个均值或方差变化点。自那以后，它已由Hawkins和邓(2010)、Ross、Tasoulis和Adams(2011)、Ross和Adams(2012)以各种方式扩展，以允许在甚至没有关于序列的分布形式的知识时，在非高斯序列中进行完全非参数变化检测。另一个扩展来自Ross和Adams(2011)和Ross(2014)，它允许分别在伯努利和指数随机变量流中检测变化，同样当参数值未知时。本文描述了R包CPM，它提供了上述所有变点模型在批处理(阶段I)和顺序(阶段II)设置中的快速实现，其中序列可以包含单个或多个变化点。
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.