Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Normality Test for High-dimensional Data based on a Nearest Neighbor
  Approach   Many statistical methodologies for high-dimensional data assume the
population is normal. Although a few multivariate normality tests have been
proposed, to the best of our knowledge, none of them can properly control the
type I error when the dimension is larger than the number of observations. In
this work, we propose a novel nonparametric test that utilizes the nearest
neighbor information. The proposed method guarantees the asymptotic type I
error control under the high-dimensional setting. Simulation studies verify the
empirical size performance of the proposed test when the dimension grows with
the sample size and at the same time exhibit a superior power performance of
the new test compared with alternative methods. We also illustrate our approach
through two popularly used data sets in high-dimensional classification and
clustering literatures where deviation from the normality assumption may lead
to invalid conclusions.

13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an