Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variable screening based on Gaussian Centered L-moments   An important challenge in big data is identification of important variables.
In this paper, we propose methods of discovering variables with non-standard
univariate marginal distributions. The conventional moments-based summary
statistics can be well-adopted for that purpose, but their sensitivity to
outliers can lead to selection based on a few outliers rather than
distributional shape such as bimodality. To address this type of
non-robustness, we consider the L-moments. Using these in practice, however,
has a limitation because they do not take zero values at the Gaussian
distributions to which the shape of a marginal distribution is most naturally
compared. As a remedy, we propose Gaussian Centered L-moments which share
advantages of the L-moments but have zeros at the Gaussian distributions. The
strength of Gaussian Centered L-moments over other conventional moments is
shown in theoretical and practical aspects such as their performances in
screening important genes in cancer genetics data.

20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph