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STOR Colloquium: Joshua Cape, Johns Hopkins

January 14, 2019 @ 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

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Details

Date:
January 14, 2019
Time:
3:30 pm - 4:30 pm
Event Category:

Venue

Hanes 120