6 papers · 1 filter
Rank-based change-point analysis for long-range dependent time series
Annika Betken, Martin Wendler
We consider change-point tests based on rank statistics to test for structural changes in long-range dependent observations. Under the hypothesis of stationary time series and unde…
Convergence of U-Processes in Hölder Spaces with Application to Robust Detection of a Changed Segment
Alfredas Račkauskas, Martin Wendler
To detect a changed segment (so called epidemic changes) in a time series, variants of the CUSUM statistic are frequently used. However, they are sensitive to outliers in the data…
Bootstrapping Covariance Operators of Functional Time Series
Olimjon Sh. Sharipov, Martin Wendler
For testing hypothesis on the covariance operator of functional time series, we suggest to use the full functional information and to avoid dimension reduction techniques. The limi…
Nuisance Parameters Free Changepoint Detection in Non-stationary Series
Michal Pešta, Martin Wendler
Detecting abrupt changes in the mean of a time series, so-called changepoints, is important for many applications. However, many procedures rely on the estimation of nuisance param…
Bootstrap for U-Statistics: A new approach
Olimjon Sh. Sharipov, Johannes Tewes, Martin Wendler
Bootstrap for nonlinear statistics like U-statistics of dependent data has been studied by several authors. This is typically done by producing a bootstrap version of the sample an…
Change-Point Detection under Dependence Based on Two-Sample U-Statistics
Herold Dehling, Roland Fried, Isabel García +1
We study the detection of change-points in time series. The classical CUSUM statistic for detection of jumps in the mean is known to be sensitive to outliers. We thus propose a rob…