collaborators

7 papers

math.ST2026

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…

stat.ME2025

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…

cs.CR2025

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…

stat.ME2025

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…

math.ST2025

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…

stat.ME2025

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…