Two-sample <i>U</i>-statistic processes for long-range dependent data

Dehling, Herold; Rooch, Aeneas; Wendler, Martin

doi:10.1080/02331888.2016.1270542

Cited by 6 publications

(8 citation statements)

References 37 publications

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“…This goes to 0 for n → ∞. Applying Chebyshev's inequality, we obtain that (7) converges to 0 in probability as n → ∞. By stationarity, this also holds for (8).…”

Section: Simulationsmentioning

confidence: 68%

“…This was proved for tests based on general U-statistics by Csörgő and Horváth (1988). This also holds under short-range dependence (Dehling, Fried, Garcia and Wendler (2015)), while under long-range dependence, the limiting distribution is given by the supremum of a linear combination of Hermite processes (see Dehling, Rooch, Wendler (2017)). For 0 < γ < 1 2 , the limiting distribution under independence is the supremum of the appropriately weighted Brownian bridge, see e.g the seminal monograph by Csörgő and Horváth (1997).…”

Section: Introductionmentioning

confidence: 84%

See 1 more Smart Citation

Change-Point Detection Under Dependence Based on Two-Sample U-Statistics

Dehling

Fried

García

et al. 2015

Asymptotic Laws and Methods in Stochastics

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We investigate the large-sample behavior of change-point tests based on weighted two-sample U-statistics, in the case of short-range dependent data. Under some mild mixing conditions, we establish convergence of the test statistic to an extreme value distribution. A simulation study shows that the weighted tests are superior to the nonweighted versions when the change-point occurs near the boundary of the time interval, while they loose power in the center.

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“…This goes to 0 for n → ∞. Applying Chebyshev's inequality, we obtain that (7) converges to 0 in probability as n → ∞. By stationarity, this also holds for (8).…”

Section: Simulationsmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 84%

Change-Point Detection Under Dependence Based on Two-Sample U-Statistics

Dehling

Fried

García

et al. 2015

Asymptotic Laws and Methods in Stochastics

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“…is the polygonal line process defined by partial sums of random variables (h 1 (X i )). Decomposition (7) reduces (h, γ)-FCLT to Hölderian invariance principle for random variables (h 1 (X i )) via the following lemma. Lemma 6.…”

Section: Double Partial Sum Processmentioning

confidence: 99%

Convergence of U-processes in Hölder spaces with application to robust detection of a changed segment

Račkauskas

Wendler

2020

Stat Papers

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To detect a changed segment (so called epedimic changes) in a time series, variants of the CUSUM statistic are frequently used. However, they are sensitive to outliers in the data and do not perform well for heavy tailed data, especially when short segments get a high weight in the test statistic. We will present a robust test statistic for epidemic changes based on the Wilcoxon statstic. To study their asymptotic behavior, we prove functional limit theorems for U -processes in Hölder spaces. We also study the finite sample behavior via simulations and apply the statistics to a real data example.If the changed segment is rather short compared to the sample size, tests who give higher weight to short segments have more power. Asymptotic critical values for such tests have been proved by Sigmund [23] in Gaussian case (see also [22]). The logarithmic case was treated in Kabluchko and Wang [14], whereas the regular varying case in Mikosch and Račkauskas [16]. Yao [26] and Hušková [12] compared tests with different wheightings. Račkauskas and Suquet [20], [21] have suggested to use a compromise weighting, that allows to express the limit distribution of the test statistic as a function of a Brownian motion. However, in order to apply the continuous mapping theorem for this statistic, it is necessary to establish the weak convergence of the partial sum process to a Brownian motion with respect to the Hölder norm.It is well known that the CUSUM statistic is sensitive to outliers in the data, see e.g. Prášková and Chochola [19]. The problem becomes worse if higher weights are given to shorter segments. A common strategy to obtain a robust change point test is to adapt robust two-sample test like the Wilcoxon one. This was first used by Darkhovsky [4] and by Pettitt [18] in the context of detecting at most one change in a sequence of independent observations. For a comparison of different change point test see Wolfe and Schechtmann [25]. The results on the Wilcoxon type change point statistic were generalized to long range dependent time series by Dehling, Rooch, Taqqu [6]. The Wilcoxon statistic can either be expressed as a rank statistic or as a (two-sample) U -statistic. This motivated Csörgő and Horváth [3] to study more general U -statistics for change point detection, followed by Ferger [9] and Gombay [11]. Orasch [17] and Döring [8] have studied U -statistics for detecting multiple change-points in a sequence of independent observations. Results for change point tests based on general two-sample U -statistics for short range dependent time series were given by Dehling, Fried, Garcia, Wendler [5], for long range dependent time series by Dehling, Rooch, Wendler [7].Gombay [10] has suggested to use a Wilcoxon type test also for the epidemic change problem. The aim of this paper is to generalize these results in three aspects: to study more general U -statistics, to allow the random variable to exhibit some form of short range dependence, and to introduce weightings to the statistic. This way, we obtain a robust test...

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“…Dehling, Fried, Garcia and Wendler [7] extended these results to weakly dependent data. Under long-range dependence, the limiting distribution is given by the supremum of a linear combination of Hermite processes; see Dehling, Rooch and Taqqu [10] for the Wilcoxon test, and Dehling, Rooch and Wendler [11] for arbitrary kernels. For 0 < γ < 1 2 , the limit distribution under independence is the supremum of the appropriately weighted Brownian bridge, see e.g.…”

Section: Introductionmentioning

confidence: 99%

Change-point detection based on weighted two-sample U-statistics

Dehling

Vuk

Wendler

2022

Electron. J. Statist.

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We investigate the large-sample behavior of change-point tests based on weighted two-sample U-statistics, in the case of short-range dependent data. Under some mild mixing conditions, we establish convergence of the test statistic to an extreme value distribution. A simulation study shows that the weighted tests are superior to the non-weighted versions when the change-point occurs near the boundary of the time interval, while they loose power in the center.

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Two-sample U-statistic processes for long-range dependent data

Cited by 6 publications

References 37 publications

Change-Point Detection Under Dependence Based on Two-Sample U-Statistics

Change-Point Detection Under Dependence Based on Two-Sample U-Statistics

Convergence of U-processes in Hölder spaces with application to robust detection of a changed segment

Change-point detection based on weighted two-sample U-statistics

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