Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence and prediction of principal component scores in
  high-dimensional settings   A number of settings arise in which it is of interest to predict Principal
Component (PC) scores for new observations using data from an initial sample.
In this paper, we demonstrate that naive approaches to PC score prediction can
be substantially biased toward 0 in the analysis of large matrices. This
phenomenon is largely related to known inconsistency results for sample
eigenvalues and eigenvectors as both dimensions of the matrix increase. For the
spiked eigenvalue model for random matrices, we expand the generality of these
results, and propose bias-adjusted PC score prediction. In addition, we compute
the asymptotic correlation coefficient between PC scores from sample and
population eigenvectors. Simulation and real data examples from the genetics
literature show the improved bias and numerical properties of our estimators.

36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
1
The categories of this paper are: cs
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int