Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalised least squares estimation of regularly varying space-time
  processes based on flexible observation schemes   Regularly varying stochastic processes model extreme dependence between
process values at different locations and/or time points. For such processes we
propose a two-step parameter estimation of the extremogram, when some part of
the domain of interest is fixed and another increasing. We provide conditions
for consistency and asymptotic normality of the empirical extremogram centred
by a pre-asymptotic version for such observation schemes. For max-stable
processes with Fr{\'e}chet margins we provide conditions, such that the
empirical extremogram (or a bias-corrected version) centred by its true version
is asymptotically normal. In a second step, for a parametric extremogram model,
we fit the parameters by generalised least squares estimation and prove
consistency and asymptotic normality of the estimates. We propose subsampling
procedures to obtain asymptotically correct confidence intervals. Finally, we
apply our results to a variety of Brown-Resnick processes. A simulation study
shows that the procedure works well also for moderate sample sizes.

23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an