Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bump detection in the presence of dependency: Does it ease or does it
  load?   We provide the asymptotic minimax detection boundary for a bump, i.e. an
abrupt change, in the mean function of a stationary Gaussian process. This will
be characterized in terms of the asymptotic behavior of the bump length and
height as well as the dependency structure of the process. A major finding is
that the asymptotic minimax detection boundary is generically determined by the
value of its spectral density at zero. Finally, our asymptotic analysis is
complemented by non-asymptotic results for AR($p$) processes and confirmed to
serve as a good proxy for finite sample scenarios in a simulation study. Our
proofs are based on laws of large numbers for non-independent and
non-identically distributed arrays of random variables and the asymptotically
sharp analysis of the precision matrix of the process.

6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics