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

Betken, Annika

doi:10.1111/jtsa.12187

Cited by 40 publications

(44 citation statements)

References 26 publications

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“…In the context of testing on a mean shift in long-memory time series the technique is applied in the sup-Wald test of Shao (2011). Betken (2016 also uses self-normalization to standardize a Wilcoxon-type test.…”

Section: Wang (2008) Extends the Results Ofmentioning

confidence: 99%

“…Altogether, the self-normalized Wilcoxon test of Betken (2016) and the fixed-b sup-Wald test of Iacone, Leybourne, and Taylor (2014) offer the best tradeoff between size control and power.…”

Section: Resultsmentioning

confidence: 99%

“…First, we consider the self-normalized Wilcoxon-type change point test by Betken (2016). It modifies the WLM test from (5) by replacing the denominator with the self-normalizer in (9), where the quantity of interest H i is the rank statistic R i = T j=1 1(y j ≤ y i ).…”

Section: Self-normalized Wilcoxon Testsmentioning

confidence: 99%

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Change-in-mean tests in long-memory time series: a review of recent developments

2018

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractIt is well known that standard tests for a mean shift are invalid in long-range dependent time series. Therefore, several long memory robust extensions of standard testing principles for a change-in-mean have been proposed in the literature. These can be divided into two groups: those that utilize consistent estimates of the long-run variance and self-normalized test statistics.Here, we review this literature and complement it by deriving a new long memory robust version of the sup-Wald test. Apart from giving a systematic review, we conduct an extensive Monte Carlo study to compare the relative performance of these methods. Special attention is paid to the interaction of the test results with the estimation of the long-memory parameter. Furthermore, we show that the power of self-normalized test statistics can be improved considerably by using an estimator that is robust to mean shifts.

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Section: Wang (2008) Extends the Results Ofmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Change-in-mean tests in long-memory time series: a review of recent developments

2018

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“…Wilcoxon-type rank tests have been studied by Wang (2008) To avoid these issues, Betken (2016) proposes an alternative normalization for the Wilcoxon change-point test. This normalization approach has originally been established by Lobato (2001) for decision on the hypothesis that a short range dependent stochastic process is uncorrelated up to a lag of a certain order.…”

Section: Recall That By the Stirling Formulamentioning

confidence: 99%

“…Under the above conditions, the critical values of the asymptotic distribution of the selfnormalized Wilcoxon test statistic are reported in Table 2 in Betken (2016). The limit of the Wilcoxon change-point test statistic can be found in Dehling et al (2013), the corresponding critical values can be taken from Table 1 in Betken (2016).…”

Section: Simulationsmentioning

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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Analyzing fusion of regularization techniques in the deep learning‐based intrusion detection system

Thakkar

Lohiya

2021

Int J of Intelligent Sys

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The surge of constantly evolving network attacks can be addressed by designing an effective and efficient Intrusion Detection System (IDS). Various Deep Learning (DL) techniques have been used for designing intelligent IDS. However, DL techniques face an issue of overfitting because of complex network structure and highdimensional data sets. Dropout and regularization are two competently perceived concepts of DL used for handling overfitting issue to enhance the performance of DL techniques. In this paper, we aim to apply fusion of various regularization techniques, namely, L1, L2, and elastic net regularization, with dropout regularization technique, for analyzing and enhancing the performance of Deep Neural Network (DNN)-based IDS. Experiments are performed using NSL-KDD, UNSW_NB-15, and CIC-IDS-2017 data sets. The value of dropout probability is derived using GridSearchCV-based hyperparameter optimization technique. Moreover, the paper also implements stateof-the-art Machine Learning techniques for the performance comparison. Apart from DNN, we have also presented performance analysis of various DL techniques, namely, Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, and Convolutional Neural Network using a fusion of regularization techniques for intrusion detection and classification. The empirical study shows that among the techniques implemented, dropout has proved to be more effective compared with L1, L2, and elastic net regularization. Moreover, fusion of dropout with other regularization techniques achieved better results compared with L1 regularization, L2 regularization, and elastic net regularization, individually. The techniques implemented for DNN-based IDS are also statistically tested using the Wilcoxon signed-rank test.

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

Cited by 40 publications

References 26 publications

Change-in-mean tests in long-memory time series: a review of recent developments

Change-in-mean tests in long-memory time series: a review of recent developments

Subsampling for General Statistics under Long Range Dependence

Analyzing fusion of regularization techniques in the deep learning‐based intrusion detection system

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