CRC 1294/1: Nonlinear Statistical Inverse Problems With Random Observations (SP A04)


The project deals with nonlinear statistical inverse problems; the goal is to estimate from random observations the functional relation between observable covariates and intrinsic (unobservable) parameters of a system whose output is observed. The system itself is known (up to the intrinsic parameters) through a specific model, e.g. a differential equation. The main objective is to design suitable non-parametric estimators using a reproducing kernel ansatz, i.e., nonlinear regularized maximum likelihood estimators, and to analyze their theoretical performance. Mechanistic modelling approaches from pharmacokinetics serve as a specific application. Questions relating to efficient computation of the estimate as well as associated quantification of uncertainty will be addressed.

Principal Investigators
Reiß, Markus Prof. Dr. (Details) (Mathematical Statistics)

Participating external organizations

Duration of Project
Start date: 07/2017
End date: 06/2021

Research Areas
Mathematics, Natural Sciences

Last updated on 2021-08-04 at 09:52