Robust Change-Point Tests
A CUSUM-type test to detect changes in the correlation.
Test statistic to detect Correlation Changes
CUSUM Test Statistic
Hodges-Lehmann Test for Change Points
Hodges Lehmann Test Statistic
Huberized CUSUM test
K-th largest element in a sum of sets.
Long Run Variance
Median of the set X - Y
Revised Modified Cholesky Factorization
Asymptotic cumulative distribution for the CUSUM Test statistic
Plot method for change point statistics
Print method for change point statistics
Cumulative sum of transformed vectors
Transformation of time series
robcp: Robust Change-Point Tests
Tests for Scale Changes Based on Pairwise Differences
Test statistic to detect Scale Changes
Weighted Median
Wilcoxon-Mann-Whitney Test Statistic for Change Points
Wilocxon-Mann-Whitney Test for Change Points
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <DOI:10.48550/arXiv.1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.