14 citations · 49 across the 14 of their papers we have counts for
14 papers
Simultaneous Nonparametric Confidence Bands for Load-Sharing Systems
Stefan Bedbur, Johann Köhne, Fabian Mies
Load-sharing systems arise in many different reliability applications, for instance, when modeling tensile strength of fibrous composites in textile industry or lifetimes of redund…
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Jörn Tebbe, Christoph Zimmer, Ansgar Steland +2
Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their cali…
Functional estimation and change detection for nonstationary time series
Fabian Mies
Tests for structural breaks in time series should ideally be sensitive to breaks in the parameter of interest, while being robust to nuisance changes. Statistical analysis thus nee…
Projection inference for high-dimensional covariance matrices with structured shrinkage targets
Fabian Mies, Ansgar Steland
Analyzing large samples of high-dimensional data under dependence is a challenging statistical problem as long time series may have change points, most importantly in the mean and…
Estimation of mixed fractional stable processes using high-frequency data
Fabian Mies, Mark Podolskij
The linear fractional stable motion generalizes two prominent classes of stochastic processes, namely stable Lévy processes, and fractional Brownian motion. For this reason it may…
Rate-optimal estimation of mixed semimartingales
Carsten H. Chong, Thomas Delerue, Fabian Mies
Consider the sum of a Brownian motion and an independent fractional Brownian motion with Hurst parameter . Even though is not a semimartinga…
Sequential Gaussian approximation for nonstationary time series in high dimensions
Fabian Mies, Ansgar Steland
Gaussian couplings of partial sum processes are derived for the high-dimensional regime . The coupling is derived for sums of independent random vectors and subsequen…
An efficient jump-diffusion approximation of the Boltzmann equation
Fabian Mies, Mohsen Sadr, Manuel Torrilhon
A jump-diffusion process along with a particle scheme is devised as an accurate and efficient particle solution to the Boltzmann equation. The proposed process (hereafter Gamma-Bol…
Confidence bands for exponential distribution functions under progressive type-II censoring
Stefan Bedbur, Fabian Mies
Based on a progressively type-II censored sample from the exponential distribution with unknown location and scale parameter, confidence bands are proposed for the underlying distr…
Regularity of multifractional moving average processes with random Hurst exponent
Dennis Loboda, Fabian Mies, Ansgar Steland
A recently proposed alternative to multifractional Brownian motion (mBm) with random Hurst exponent is studied, which we refer to as Itô-mBm. It is shown that Itô-mBm is locally se…
Exact Semiparametric Inference and Model Selection for Load-Sharing Systems
Fabian Mies, Stefan Bedbur
As a specific proportional hazard rates model, sequential order statistics can be used to describe the lifetimes of load-sharing systems. Inference for these systems needs to accou…
Rate-optimal estimation of the Blumenthal-Getoor index of a Lévy process
Fabian Mies
The Blumenthal-Getoor (BG) index characterizes the jump measure of an infinitely active Lévy process. It determines sample path properties and affects the behavior of various econo…
Estimation of state-dependent jump activity and drift for Markovian semimartingales
Fabian Mies
The jump behavior of an infinitely active Itô semimartingale can be conveniently characterized by a jump activity index of Blumenthal-Getoor type, typically assumed to be constant…
Computational Methods for Path-based Robust Flows
Fabian Mies, Britta Peis, Andreas Wierz
Real world networks are often subject to severe uncertainties which need to be addressed by any reliable prescriptive model. In the context of the maximum flow problem subject to a…