A Likelihood Ratio Approach to Sequential Change Point Detection for a General Class of Parameters

Holger Dette and Josua Gösmann

Journal of the American Statistical Association, 2020, vol. 115, issue 531, 1361-1377

Abstract: In this article, we propose a new approach for sequential monitoring of a general class of parameters of a d-dimensional time series, which can be estimated by approximately linear functionals of the empirical distribution function. We consider a closed-end method, which is motivated by the likelihood ratio test principle and compare the new method with two alternative procedures. We also incorporate self-normalization such that estimation of the long-run variance is not necessary. We prove that for a large class of testing problems the new detection scheme has asymptotic level α and is consistent. The asymptotic theory is illustrated for the important cases of monitoring a change in the mean, variance, and correlation. By means of a simulation study it is demonstrated that the new test performs better than the currently available procedures for these problems. Finally, the methodology is illustrated by a small data example investigating index prices from the dot-com bubble. Supplementary materials for this article are available online.

Date: 2020
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:115:y:2020:i:531:p:1361-1377

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DOI: 10.1080/01621459.2019.1630562

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