Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The bootstrap, covariance matrices and PCA in moderate and
  high-dimensions   We consider the properties of the bootstrap as a tool for inference
concerning the eigenvalues of a sample covariance matrix computed from an
$n\times p$ data matrix $X$. We focus on the modern framework where $p/n$ is
not close to 0 but remains bounded as $n$ and $p$ tend to infinity.
  Through a mix of numerical and theoretical considerations, we show that the
bootstrap is not in general a reliable inferential tool in the setting we
consider. However, in the case where the population covariance matrix is
well-approximated by a finite rank matrix, the bootstrap performs as it does in
finite dimension.

30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
1
The categories of this paper are: cs
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an