Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive estimating function inference for non-stationary determinantal
  point processes   Estimating function inference is indispensable for many common point process
models where the joint intensities are tractable while the likelihood function
is not. In this paper we establish asymptotic normality of estimating function
estimators in a very general setting of non-stationary point processes. We then
adapt this result to the case of non-stationary determinantal point processes
which are an important class of models for repulsive point patterns. In
practice often first and second order estimating functions are used. For the
latter it is common practice to omit contributions for pairs of points
separated by a distance larger than some truncation distance which is usually
specified in an ad hoc manner. We suggest instead a data-driven approach where
the truncation distance is adapted automatically to the point process being
fitted and where the approach integrates seamlessly with our asymptotic
framework. The good performance of the adaptive approach is illustrated via
simulation studies for non-stationary determinantal point processes and by an
application to a real dataset.

18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an