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STOR Colloquium: Joshua Cape, Johns Hopkins

14 Jan @ 3:30 pm - 4:30 pm

Joshua Cape
Johns Hopkins University


Statistical analysis and spectral methods for signal-plus-noise matrix models


Estimating eigenvectors and principal subspaces is of fundamental importance for numerous problems in statistics, data science, and network analysis, including covariance matrix estimation, principal component analysis, and community detection. For each of these problems, we obtain foundational results that precisely quantify the local (e.g., entrywise) behavior of sample eigenvectors within the context of a unified signal-plus-noise matrix framework. Our methods and results collectively address eigenvector consistency and asymptotic normality, decompositions of high-dimensional matrices, Procrustes analysis, deterministic perturbation bounds, and real-data spectral clustering applications in connectomics.






Refreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall


14 Jan
3:30 pm - 4:30 pm
Event Category:


120 Hanes Hall
Hanes Hall
Chapel Hill, NC 27599 United States