Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse semiparametric canonical correlation analysis for data of mixed
  types   Canonical correlation analysis investigates linear relationships between two
sets of variables, but often works poorly on modern data sets due to
high-dimensionality and mixed data types such as continuous, binary and
zero-inflated. To overcome these challenges, we propose a semiparametric
approach for sparse canonical correlation analysis based on Gaussian copula.
Our main contribution is a truncated latent Gaussian copula model for data with
excess zeros, which allows us to derive a rank-based estimator of the latent
correlation matrix for mixed variable types without the estimation of marginal
transformation functions. The resulting canonical correlation analysis method
works well in high-dimensional settings as demonstrated via numerical studies,
as well as in application to the analysis of association between gene
expression and micro RNA data of breast cancer patients.

1
The categories of this paper are: cs
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol