npcp

R package npcp: Some Nonparametric Tests for Change-Point Detection in Possibly Multivariate Observations. Provides nonparametric tests for assessing whether possibly serially dependent univariate or multivariate observations have the same c.d.f. or not. In addition to tests focusing directly on the c.d.f., the package contains tests designed to be particularly sensitive to changes in the underlying copula, Spearman’s rho or certain quantities that can be estimated using one-sample U-statistics of order two such as the variance, Gini’s mean difference or Kendall’s tau. The latest addition is a nonparametric test for detecting changes in the distribution of independent block maxima.


References in zbMATH (referenced in 10 articles )

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  1. Fisher, Thomas J.; Zhang, Jing; Colegate, Stephen P.; Vanni, Michael J.: Detecting and modeling changes in a time series of proportions (2022)
  2. Nasri, Bouchra R.; Rémillard, Bruno N.; Bahraoui, Tarik: Change-point problems for multivariate time series using pseudo-observations (2022)
  3. Holmes, Mark; Kojadinovic, Ivan: Open-end nonparametric sequential change-point detection based on the retrospective CUSUM statistic (2021)
  4. Kojadinovic, Ivan; Verdier, Ghislain: Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions (2021)
  5. Bücher, Axel; Fermanian, Jean-David; Kojadinovic, Ivan: Combining cumulative sum change-point detection tests for assessing the stationarity of univariate time series (2019)
  6. Hofert, Marius; Kojadinovic, Ivan; Mächler, Martin; Yan, Jun: Elements of copula modeling with R (2018)
  7. Bücher, Axel; Kinsvater, Paul; Kojadinovic, Ivan: Detecting breaks in the dependence of multivariate extreme-value distributions (2017)
  8. Kojadinovic, Ivan; Naveau, Philippe: Detecting distributional changes in samples of independent block maxima using probability weighted moments (2017)
  9. Kojadinovic, Ivan; Quessy, Jean-François; Rohmer, Tom: Testing the constancy of Spearman’s rho in multivariate time series (2016)
  10. Bücher, Axel; Kojadinovic, Ivan; Rohmer, Tom; Segers, Johan: Detecting changes in cross-sectional dependence in multivariate time series (2014)