Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semiparametric Sparse Discriminant Analysis   In recent years, a considerable amount of work has been devoted to
generalizing linear discriminant analysis to overcome its incompetence for
high-dimensional classification (Witten & Tibshirani 2011, Cai & Liu 2011, Mai
et al. 2012, Fan et al. 2012). In this paper, we develop high-dimensional
semiparametric sparse discriminant analysis (HD-SeSDA) that generalizes the
normal-theory discriminant analysis in two ways: it relaxes the Gaussian
assumptions and can handle non-polynomial (NP) dimension classification
problems. If the underlying Bayes rule is sparse, HD-SeSDA can estimate the
Bayes rule and select the true features simultaneously with overwhelming
probability, as long as the logarithm of dimension grows slower than the cube
root of sample size. Simulated and real examples are used to demonstrate the
finite sample performance of HD-SeSDA. At the core of the theory is a new
exponential concentration bound for semiparametric Gaussian copulas, which is
of independent interest.

20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg