Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Asymptotic robustness of Kelly's GLRT and Adaptive Matched Filter
  detector under model misspecification   A fundamental assumption underling any Hypothesis Testing (HT) problem is
that the available data follow the parametric model assumed to derive the test
statistic. Nevertheless, a perfect match between the true and the assumed data
models cannot be achieved in many practical applications. In all these cases,
it is advisable to use a robust decision test, i.e. a test whose statistic
preserves (at least asymptotically) the same probability density function (pdf)
for a suitable set of possible input data models under the null hypothesis.
Building upon the seminal work of Kent (1982), in this paper we investigate the
impact of the model mismatch in a recurring HT problem in radar signal
processing applications: testing the mean of a set of Complex Elliptically
Symmetric (CES) distributed random vectors under a possible misspecified,
Gaussian data model. In particular, by using this general misspecified
framework, a new look to two popular detectors, the Kelly's Generalized
Likelihood Ration Test (GLRT) and the Adaptive Matched Filter (AMF), is
provided and their robustness properties investigated.

34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom