Nonparametric Monitoring of Data Streams for Changes in Location and Scale

Ross, Gordon J.; Tasoulis, Dimitris K.; Adams, Niall M.

doi:10.1198/tech.2011.10069

Cited by 182 publications

(160 citation statements)

References 20 publications

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“…Numerous CPMs have so far been developed to be applied within this framework to suit the conditions of variously distributed data: parametric, with breakpoints of di erent origin such as shifts in mean or variance [51], or non-parametric [52], as well.…”

Section: Sequential Changepoint Detection Via the Cpm Methodsmentioning

confidence: 99%

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

et al. 2016

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Time series often contain breakpoints of di erent origin, i.e. breakpoints, caused by (i) shifts in trend, (ii) other changes in trend and/or, (iii) changes in variance. In the present study, arti cially generated time series with white and red noise structures are analyzed using three recently developed breakpoint detection methods. The time series are modi ed so that the exact "locations" of the arti cial breakpoints are prescribed, making it possible to evaluate the methods exactly. Hence, the study provides a deeper insight into the behaviour of the three di erent breakpoint detection methods. Utilizing this experience can help solving breakpoint detection problems in reallife data sets, as is demonstrated with two examples taken from the elds of paleoclimate research and petrology.

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Section: Sequential Changepoint Detection Via the Cpm Methodsmentioning

confidence: 99%

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

et al. 2016

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“…For further information about used test statistics and their exact application in parametric and non-parametric we refer to Ross et al (2011) and Ross and Adam (2012).…”

Section: Non-gaussian Sequence Modelsmentioning

confidence: 99%

Structural Distress Index: Structural Break Analysis of the Czech and Polish Stock Markets

Princ¹

2016

EFAJ

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Abstract:The estimation of multiple structural break models is usually associated with identification of spurious break points, which are identified by universal algorithms. This leads to overvaluation of structural distress in financial markets represented by data series. The paper is focused on an estimation of the new index, which incorporates results of Student, Bartlett, GLR, Mann-Whitney, Mood, Lepage, Kolmogorov-Smirnov and finally Cramer-von-Mises tests statistics together. The new measure is named Structural Distress Index and evaluates a probability of structural break occurrence based on estimations of proposed models. SDI values show that Czech and Polish stock markets went through more instable period in 1990s than at the beginning of the global financial crisis in 2007. SDI measure is straightforward and can be easily explained, the highest values of SDI can identify the most important break points of the research period, which starts in year 1993 and ends in year 2014. Universality of SDI offers its further extension and application to further research of financial markets.

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“…When the task is to detect a change in a data sequence where no information is available regarding the pre-or post-change distribution, the approach of Ross et al (2011) is to replace D k,t with a nonparametric two-sample test statistic that can detect arbitrary changes in a distribution. The algorithm would proceed as above, with this statistic evaluated at every time point, and the maximum value being compared to a threshold h t .…”

Section: A Nonparametric Approachmentioning

confidence: 99%

“…The location of the change point is unknown, and the problem is to detect it as soon as possible. The change point methodology can also be applied to sequences that are not iid between change points, by rst modeling the data sequence in a way that yields iid one-step-ahead forecast residuals and then performing change detection on these (Ross et al, 2011).…”

Section: Change Point Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation

Nystrup¹,

Hansen²,

Madsen³

et al. 2016

J Asset Manag

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Nonparametric Monitoring of Data Streams for Changes in Location and Scale

Cited by 182 publications

References 20 publications

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

Detecting breakpoints in artificially modified- and real-life time series using three state-of-the-art methods

Structural Distress Index: Structural Break Analysis of the Czech and Polish Stock Markets

Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation

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