1 citations · 2 across the 14 of their papers we have counts for
14 papers
Change Point Detection and Localization in High-Dimensional Time Series
Patrick Bastian, Daria Tieplova, Nina Dörnemann +1
We present new inference tools for change point detection in high-dimensional time series. We discuss two distinct statistical applications: First, sequential change point testing…
Selfnormalization for relevant inference with supremum-type statistics
Patrick Bastian
We develop a selfnormalized approach to inference for relevant changes in functional time series measured by the supremum norm. The main difficulty is that the supremum norm is not…
Differentially private testing for relevant dependencies in high dimensions
Patrick Bastian, Holger Dette, Martin Dunsche
We investigate the problem of detecting dependencies between the components of a high-dimensional vector. Our approach advances the existing literature in two important respects. F…
Monitoring Time Series for Relevant Changes
Patrick Bastian, Tim Kutta, Rupsa Basu +1
We consider the problem of sequentially testing for changes in the mean parameter of a time series, compared to a benchmark period. Most tests in the literature focus on the null h…
SILENT: A New Lens on Statistics in Software Timing Side Channels
Martin Dunsche, Patrick Bastian, Marcel Maehren +5
Cryptographic research takes software timing side channels seriously. Approaches to mitigate them include constant-time coding and techniques to enforce such practices. However, re…
Multiscale detection of practically significant changes in a gradually varying time series
Patrick Bastian, Holger Dette
In many change point problems it is reasonable to assume that compared to a benchmark at a given time point the properties of the observed stochastic process change gradually…
Detecting relevant dependencies under measurement error with applications to the analysis of planetary system evolution
Patrick Bastian, Nicolai Bissantz
Exoplanets play an important role in understanding the mechanics of planetary system formation and orbital evolution. In this context the correlations of different parameters of th…
Sequential Outlier Detection in Non-Stationary Time Series
Florian Heinrichs, Patrick Bastian, Holger Dette
A novel method for sequential outlier detection in non-stationary time series is proposed. The method tests the null hypothesis of ``no outlier'' at each time point, addressing the…
Uniform confidence bands for joint angles across different fatigue phases
Patrick Bastian, Rupsa Basu, Holger Dette
We develop uniform confidence bands for the mean function of stationary time series as a post-hoc analysis of multiple change point detection in functional time series. In particul…
Gradual changes in functional time series
Patrick Bastian, Holger Dette
We consider the problem of detecting gradual changes in the sequence of mean functions from a not necessarily stationary functional time series. Our approach is based on the maximu…
Multiple change point detection in functional data with applications to biomechanical fatigue data
Patrick Bastian, Rupsa Basu, Holger Dette
Injuries to the lower extremity joints are often debilitating, particularly for professional athletes. Understanding the onset of stressful conditions on these joints is therefore…
Testing equivalence of multinomial distributions -- a constrained bootstrap approach
Patrick Bastian, Holger Dette, Lukas Koletzko
In this paper we develop a novel bootstrap test for the comparison of two multinomial distributions. The two distributions are called {\it equivalent} or {\it similar} if a norm of…
Comparing regression curves -- an -point of view
Patrick Bastian, Holger Dette, Lukas Koletzko +1
In this paper we compare two regression curves by measuring their difference by the area between the two curves, represented by their -distance. We develop asymptotic confiden…
Testing for practically significant dependencies in high dimensions via bootstrapping maxima of U-statistics
Patrick Bastian, Holger Dette, Johannes Heiny
This paper takes a different look on the problem of testing the mutual independence of the components of a high-dimensional vector. Instead of testing if all pairwise associations…