1 citations · 1 across the 1 of their papers we have counts for
Showing math.PRShow all
3 papers · 1 filter
math.PR2020★ 1 cited
High dimensional normality of noisy eigenvectors
Jake Marcinek, Horng-Tzer Yau
We study joint eigenvector distributions for large symmetric matrices in the presence of weak noise. Our main result asserts that every submatrix in the orthogonal matrix of eigenv…
math.PR2020
Eigenvector Statistics of Lévy Matrices
Amol Aggarwal, Patrick Lopatto, Jake Marcinek
We analyze statistics for eigenvector entries of heavy-tailed random symmetric matrices (also called Lévy matrices) whose associated eigenvalues are sufficiently small. We show tha…
math.PR2018
Comparison theorem for some extremal eigenvalue statistics
Benjamin Landon, Patrick Lopatto, Jake Marcinek
We introduce a method for the comparison of some extremal eigenvalue statistics of random matrices. For example, it allows one to compare the maximal eigenvalue gap in the bulk of…