Structural inference for high-dimensional covariance matrices

• Antragstellerinnen / Antragsteller: Professor Dr. Holger Dette; Professorin Dr. Angelika Rohde
• Fachliche Zuordnung: Mathematik
• Förderung: Förderung von 2012 bis 2018
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 197645397

We are concerned with estimation and inference for high-dimensional covariance matrices understructural 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 thestatistical procedures meaningful even if the dimension of the matrix is large as compared to thesample size. Key issue is a profound understanding of the spectral properties of correspondingestimators tailored to these sparsity constraints in order to perform efficient adaptive inference forhigh-dimensional data. Applications include volatility estimation in high-dimensional portfolios.

• DFG-Verfahren: Forschungsgruppen
• Teilprojekt zu: FOR 1735: Strukturelle Inferenz in der Statistik: Adaption und Effizienz