Data driven rank test for the change point problem

Antoch, Jaromı́r; Hušková, Marie; Janic, Alicja; Ledwina, Teresa

doi:10.1007/s00184-007-0139-2

Cited by 20 publications

(21 citation statements)

References 11 publications

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“…In this article, we consider a self‐normalized rank test that serves to identify change‐points in the mean of LRD data. So‐called max‐type rank tests are introduced by Antoch et al () for detecting changes in the distribution function of i.i.d. random variables.…”

Section: Introductionmentioning

confidence: 99%

Testing for Change‐Points in Long‐Range Dependent Time Series by Means of a Self‐Normalized Wilcoxon Test

Betken

2016

Journal Time Series Analysis

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We propose a testing procedure based on the Wilcoxon two-sample test statistic in order to test for change-points in the mean of long-range dependent data. We show that the corresponding self-normalized test statistic converges in distribution to a non-degenerate limit under the hypothesis that no change occurred and that it diverges to infinity under the alternative of a change-point with constant height. Furthermore, we derive the asymptotic distribution of the self-normalized Wilcoxon test statistic under local alternatives, that is, under the assumption that the height of the level shift decreases as the sample size increases. Regarding the finite sample performance, simulation results confirm that the self-normalized Wilcoxon test yields a consistent discrimination between hypothesis and alternative and that its empirical size is already close to the significance level for moderate sample sizes.

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Section: Introductionmentioning

confidence: 99%

Testing for Change‐Points in Long‐Range Dependent Time Series by Means of a Self‐Normalized Wilcoxon Test

Betken

2016

Journal Time Series Analysis

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“…In practice one usually chooses p N = c log N, c > 0, where suitable choice of c is discussed in [1].…”

Section: Resultsmentioning

confidence: 99%

“…we can see that the newly proposed test procedure slightly improves the situation if the desired level of the test would be α 1 . On the other hand, if we wish to keep the composite level α, it appears that for the sample size n = 200 and selected parameters of the simulation the original procedure from [1] is still slightly better. The reason seems to be the fact that asymptotic results do not apply for such small sample size as is, unfortunately, often the case in the change point detection.…”

Section: Simulation Studymentioning

confidence: 99%

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Data driven rank statistics in change point analysis

Antoch

Hušková

2009

Mathematica Slovaca

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“…Rank-based tests were introduced by Antoch et al (2008) for detecting changes in the distribution function of independent random variables.…”

Section: Recall That By the Stirling Formulamentioning

confidence: 99%

Subsampling for General Statistics under Long Range Dependence

Betken¹,

Wendler²

2018

STAT SINICA

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Abstract. In the statistical inference for long range dependent time series the shape of the limit distribution typically depends on unknown parameters. Therefore, we propose to use subsampling. We show the validity of subsampling for general statistics and long range dependent subordinated Gaussian processes which satisfy mild regularity conditions. We apply our method to a self-normalized change-point test statistic so that we can test for structural breaks in long range dependent time series without having to estimate any nuisance parameter. The finite sample properties are investigated in a simulation study. We analyze three data sets and compare our results to the conclusions of other authors.

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Data driven rank test for the change point problem

Abstract: Change point, Data driven test, Model selection, Rank test,

Cited by 20 publications

References 11 publications

Testing for Change‐Points in Long‐Range Dependent Time Series by Means of a Self‐Normalized Wilcoxon Test

Testing for Change‐Points in Long‐Range Dependent Time Series by Means of a Self‐Normalized Wilcoxon Test

Data driven rank statistics in change point analysis

Subsampling for General Statistics under Long Range Dependence

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