Testing for multiple change points

Jaromír Antoch () and Daniela Jarušková ()

Computational Statistics, 2013, vol. 28, issue 5, 2183 pages

Abstract: In this paper we concentrate on testing for multiple changes in the mean of a series of independent random variables. Suggested method applies a maximum type test statistic. Our primary focus is on an effective calculation of critical values for very large sample sizes comprising (tens of) thousands of observations and a moderate to large number of segments. To that end, Monte Carlo simulations and a modified Bellman’s principle of optimality are used. It is shown that, indisputably, computer memory becomes a critical bottleneck in solving a problem of such a size. Thus, minimization of the memory requirements and appropriate order of calculations appear to be the keys to success. In addition, the formula that can be used to get approximate asymptotic critical values using the theory of exceedance probability of Gaussian fields over a high level is presented. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Testing for multiple change-points; Segmentation; Maximum type test statistic; Monte Carlo simulations; Dynamic programming principle; Asymptotic distribution; Approximate critical values; Extremes of Gaussian fields (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:5:p:2161-2183

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DOI: 10.1007/s00180-013-0401-1

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