Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Outlier detection in multivariate functional data through a contaminated
  mixture model   In an industrial context, the activity of sensors is recorded at a high
frequency. A challenge is to automatically detect abnormal measurement
behavior. Considering the sensor measures as functional data, the problem can
be formulated as the detection of outliers in a multivariate functional data
set. Due to the heterogeneity of this data set, the proposed contaminated
mixture model both clusters the multivariate functional data into homogeneous
groups and detects outliers. The main advantage of this procedure over its
competitors is that it does not require to specify the proportion of outliers.
Model inference is performed through an Expectation-Conditional Maximization
algorithm, and the BIC is used to select the number of clusters. Numerical
experiments on simulated data demonstrate the high performance achieved by the
inference algorithm. In particular, the proposed model outperforms the
competitors. Its application on the real data which motivated this study allows
to correctly detect abnormal behaviors.

37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ