Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Extreme-Value Approach for Testing the Equality of Large U-Statistic
  Based Correlation Matrices   There has been an increasing interest in testing the equality of large
Pearson's correlation matrices. However, in many applications it is more
important to test the equality of large rank-based correlation matrices since
they are more robust to outliers and nonlinearity. Unlike the Pearson's case,
testing the equality of large rank-based statistics has not been well explored
and requires us to develop new methods and theory. In this paper, we provide a
framework for testing the equality of two large U-statistic based correlation
matrices, which include the rank-based correlation matrices as special cases.
Our approach exploits extreme value statistics and the Jackknife estimator for
uncertainty assessment and is valid under a fully nonparametric model.
Theoretically, we develop a theory for testing the equality of U-statistic
based correlation matrices. We then apply this theory to study the problem of
testing large Kendall's tau correlation matrices and demonstrate its
optimality. For proving this optimality, a novel construction of least
favourable distributions is developed for the correlation matrix comparison.

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The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph