Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Asymptotic normality for eigenvalue statistics of a general sample
  covariance matrix when $p/n \to \infty$ and applications   The asymptotic normality for a large family of eigenvalue statistics of a
general sample covariance matrix is derived under the ultra-high dimensional
setting, that is, when the dimension to sample size ratio $p/n \to \infty$.
Based on this CLT result, we first adapt the covariance matrix test problem to
the new ultra-high dimensional context. Then as a second application, we
develop a new test for the separable covariance structure of a matrix-valued
white noise. Simulation experiments are conducted for the investigation of
finite-sample properties of the general asymptotic normality of eigenvalue
statistics, as well as the second test for separable covariance structure of
matrix-valued white noise.

21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
1
The categories of this paper are: cs
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas