Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Non-linear Wiener-Granger Causality For Large High-dimensional
  Data   Wiener-Granger causality is a widely used framework of causal analysis for
temporally resolved events. We introduce a new measure of Wiener-Granger
causality based on kernelization of partial canonical correlation analysis with
specific advantages in the context of large high-dimensional data. The
introduced measure is able to detect non-linear and non-monotonous signals, is
designed to be immune to noise, and offers tunability in terms of computational
complexity in its estimations. Furthermore, we show that, under specified
conditions, the introduced measure can be regarded as an estimate of
conditional mutual information (transfer entropy). The functionality of this
measure is assessed using comparative simulations where it outperforms other
existing methods. The paper is concluded with an application to climatological
data.

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The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio