1 citations · 1 across the 1 of their papers we have counts for
4 papers
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…
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…
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…
KMS weights on higher rank buildings
Jake Marcinek, Matilde Marcolli
We extend some of the results of Carey-Marcolli-Rennie on modular index invariants of Mumford curves to the case of higher rank buildings: we discuss notions of KMS weights on buil…