"Identifiability and structural inference for high-dimensional diffusion matrices"


We will develop theory and an estimation methodology for the analysis of sparsely parametrised high-dimensional diffusion matrices, including in particular identifiability issues. This requires the combination of adaptive smoothing techniques and sparsity inducing penalization methods and results in a challenging simultaneous adaptation problem. The analysis is supposed to provide more fundamental insight even for more classical situations for independent and identically distributed data.


Principal Investigators
Reiß, Markus Prof. Dr. (Details) (Research Groups (DFG))

Duration of Project
Start date: 04/2012
End date: 03/2015

Last updated on 2020-11-03 at 23:19