Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Research Unit 1735

Identifiability and structural inference for highdimensional diffusion matrices

This project is concerned with estimation and inference for high dimensional covariance matrices under structural constraints. We focus on banded matrices and matrices with a block diagonal structure. Structural constraints of this type induce a considerable complexity reduction which renders the statistical procedures meaningful even if the dimension of the matrix is large as compared to the sample size. Key issue is a profound understanding of the spectral properties of corresponding estimators tailored to these sparsity constraints in order to perform efficient adaptive inference for high-dimensional data.


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