Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A nonparametric estimator of the spectral density of a continuous-time
  Gaussian process observed at random times   In numerous applications data are observed at random times and an estimated
graph of the spectral density may be relevant for characterizing and explaining
phenomena. By using a wavelet analysis, one derives a nonparametric estimator
of the spectral density of a Gaussian process with stationary increments (or a
stationary Gaussian process) from the observation of one path at random
discrete times. For every positive frequency, this estimator is proved to
satisfy a central limit theorem with a convergence rate depending on the
roughness of the process and the moment of random durations between successive
observations. In the case of stationary Gaussian processes, one can compare
this estimator with estimators based on the empirical periodogram. Both
estimators reach the same optimal rate of convergence, but the estimator based
on wavelet analysis converges for a different class of random times. Simulation
examples and application to biological data are also provided.

19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys