Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Corrections to LRT on Large Dimensional Covariance Matrix by RMT   In this paper, we give an explanation to the failure of two likelihood ratio
procedures for testing about covariance matrices from Gaussian populations when
the dimension is large compared to the sample size. Next, using recent central
limit theorems for linear spectral statistics of sample covariance matrices and
of random F-matrices, we propose necessary corrections for these LR tests to
cope with high-dimensional effects. The asymptotic distributions of these
corrected tests under the null are given. Simulations demonstrate that the
corrected LR tests yield a realized size close to nominal level for both
moderate p (around 20) and high dimension, while the traditional LR tests with
chi-square approximation fails. Another contribution from the paper is that for
testing the equality between two covariance matrices, the proposed correction
applies equally for non-Gaussian populations yielding a valid pseudo-likelihood
ratio test.

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The categories of this paper are: chem-ph
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an