2 citations · 2 across the 15 of their papers we have counts for
17 papers
Simultaneous Inference for Partially Observed Functional Time Series
Patrick Bastian, Tim Kutta
Functional data analysis (FDA) provides statistical methods for analyzing samples of time-continuous stochastic processes. Measurements often arise in the form of sensor data for a…
Sequential Auditing for f-Differential Privacy
Tim Kutta, Martin Dunsche, Yu Wei +1
We present new auditors to assess Differential Privacy (DP) of an algorithm based on output samples. Such empirical auditors are common to check for algorithmic correctness and imp…
Inference for Forecasting Accuracy: Pooled versus Individual Estimators in High-dimensional Panel Data
Tim Kutta, Martin Schumann, Holger Dette
Panels with large time and cross-sectional dimensions are a key data structure in social sciences and other fields. A central question in panel data analysis is whether…
Multiscale Change Point Detection for Functional Time Series
Tim Kutta, Holger Dette, Shixuan Wang
We study the problem of detecting and localizing multiple changes in the mean parameter of a Banach space-valued time series. The goal is to construct a collection of narrow confid…
TWIN: Two window inspection for online change point detection
Patrick Bastian, Tim Kutta
We propose a new class of sequential change point tests, both for changes in the mean parameter and in the overall distribution function. The methodology builds on a two-window ins…
Monitoring Violations of Differential Privacy over Time
Önder Askin, Tim Kutta, Holger Dette
Auditing differential privacy has emerged as an important area of research that supports the design of privacy-preserving mechanisms. Privacy audits help to obtain empirical estima…
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…
General-Purpose -DP Estimation and Auditing in a Black-Box Setting
Önder Askin, Holger Dette, Martin Dunsche +4
In this paper we propose new methods to statistically assess -Differential Privacy (-DP), a recent refinement of differential privacy (DP) that remedies certain weaknesses of…
Testing separability for continuous functional data
Holger Dette, Gauthier Dierickx, Tim Kutta
Analyzing the covariance structure of data is a fundamental task of statistics. While this task is simple for low-dimensional observations, it becomes challenging for more intricat…
Lower Bounds for Rényi Differential Privacy in a Black-Box Setting
Tim Kutta, Önder Askin, Martin Dunsche
We present new methods for assessing the privacy guarantees of an algorithm with regard to Rényi Differential Privacy. To the best of our knowledge, this work is the first to addre…
Validating Approximate Slope Homogeneity in Large Panels
Tim Kutta, Holger Dette
Statistical inference for large data panels is omnipresent in modern economic applications. An important benefit of panel analysis is the possibility to reduce noise and thus to gu…
Multivariate Mean Comparison under Differential Privacy
Martin Dunsche, Tim Kutta, Holger Dette
The comparison of multivariate population means is a central task of statistical inference. While statistical theory provides a variety of analysis tools, they usually do not prote…
Statistical inference for the slope parameter in functional linear regression
Tim Kutta, Gauthier Dierickx, Holger Dette
In this paper we consider the linear regression model with functional regressors and responses. We develop new inference tools to quantify deviations of the t…
Statistical Quantification of Differential Privacy: A Local Approach
Önder Askin, Tim Kutta, Holger Dette
In this work, we introduce a new approach for statistical quantification of differential privacy in a black box setting. We present estimators and confidence intervals for the opti…
Quantifying deviations from separability in space-time functional processes
Holger Dette, Gauthier Dierickx, Tim Kutta
The estimation of covariance operators of spatio-temporal data is in many applications only computationally feasible under simplifying assumptions, such as separability of the cova…
Detecting structural breaks in eigensystems of functional time series
Holger Dette, Tim Kutta
Detecting structural changes in functional data is a prominent topic in statistical literature. However not all trends in the data are important in applications, but only those of…
The empirical process of residuals from an inverse regression
Tim Kutta, Nicolai Bissantz, Justin Chown +1
In this paper we investigate an indirect regression model characterized by the Radon transformation. This model is useful for recovery of medical images obtained by computed tomogr…