  Recently, Chernozhukov, Chetverikov, and Kato [Ann. Statist. 42 (2014)
1564--1597] developed a new Gaussian comparison inequality for approximating
the suprema of empirical processes. This paper exploits this technique to
devise sharp inference on spectra of large random matrices. In particular, we
show that two long-standing problems in random matrix theory can be solved: (i)
simple bootstrap inference on sample eigenvalues when true eigenvalues are
tied; (ii) conducting two-sample Roy's covariance test in high dimensions. To
establish the asymptotic results, a generalized $\epsilon$-net argument
regarding the matrix rescaled spectral norm and several new empirical process
bounds are developed and of independent interest.
