B1: Statistical Inference in Inverse Problems with Qualitative Prior Information

This project aims for the development and analysis of statistical regularization methods in the context of noisy inverse problems with specific prior information, given by shape constraints such as monotonicity, positivity and… (more)

B2: Nonlinear Inverse Problems with Noisy Operators

In this project we study iterative regularization methods, in particular regularized Newton methods for nonlinear inverse problems with a forward operator perturbed by random noise. We will derive convergence rate… (more)

B3: Statistical Multiscale Parameter Selection Strategies

Parameter selection is a final but very important step in any (statistical) regularization process in order to determine the level of resolution of a given regularized reconstruction in a statistical inverse problem. In this project we… (more)

B4: Inference for Semimartingale Stochastic Volatility Models

Combining statistical methods from discretely observed stochastic processes with regression, inverse problem and bootstrap techniques our aim in this project is to derive new estimating procedures for the different quantities… (more)

B5: Partial least squares for serially dependent data

This projects focuses on the partial least squares (PLS) - a type of regularized least squares regression developed for ill-conditioned linear regression models. (more)