Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sequential Change-point Detection for High-dimensional and non-Euclidean
  Data   In many modern applications, high-dimensional/non-Euclidean data sequences
are collected to study complicated phenomena over time and it is of scientific
importance to detect anomaly events as the data are being collected. We studied
a nonparametric framework that utilizes nearest neighbor information among the
observations and can be applied to such sequences. We considered new test
statistics under this framework that can make more positive detections and can
detect anomaly events sooner than the existing test under many common scenarios
with the false discovery rate controlled at the same level. Analytic formulas
for approximate the average run lengths of the new approaches are derived to
make them fast applicable to large datasets.

11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
1
The categories of this paper are: cs
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph