Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Asymptotic properties of Principal Component Analysis and shrinkage-bias
  adjustment under the Generalized Spiked Population model   With the development of high-throughput technologies, principal component
analysis (PCA) in the high-dimensional regime is of great interest. Most of the
existing theoretical and methodological results for high-dimensional PCA are
based on the spiked population model in which all the population eigenvalues
are equal except for a few large ones. Due to the presence of local correlation
among features, however, this assumption may not be satisfied in many
real-world datasets. To address this issue, we investigated the asymptotic
behaviors of PCA under the generalized spiked population model. Based on the
theoretical results, we proposed a series of methods for the consistent
estimation of population eigenvalues, angles between the sample and population
eigenvectors, correlation coefficients between the sample and population
principal component (PC) scores, and the shrinkage bias adjustment for the
predicted PC scores. Using numerical experiments and real data examples from
the genetics literature, we showed that our methods can greatly reduce bias and
improve prediction accuracy.

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The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph