Quick inference for log Gaussian Cox processes with non-stationary underlying random fields

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F19%3A43899299" target="_blank" >RIV/60076658:12510/19:43899299 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11320/19:10401361

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2211675319301393" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2211675319301393</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.spasta.2019.100388" target="_blank" >10.1016/j.spasta.2019.100388</a>

Result language

angličtina

Original language name

Quick inference for log Gaussian Cox processes with non-stationary underlying random fields

Original language description

For point patterns observed in natura, spatial heterogeneity is more the rule than the exception. In numerous applications, this can be mathematically handled by the flexible class of log Gaussian Cox processes (LGCPs); in brief, a LGCP is a Cox process driven by an underlying log Gaussian random field (log GRF). This allows the representation of point aggregation, point vacuum and intermediate situations, with more or less rapid transitions between these different states depending on the properties of GRF. Very often, the covariance function of the GRF is assumed to be stationary. In this article, we give two examples where the sizes (that is, the number of points) and the spatial extents of point clusters are allowed to vary in space. To tackle such features, we propose parametric and semiparametric models of non-stationary LGCPs where the non-stationarity is included in both the mean function and the covariance function of the GRF. Thus, in contrast to most other work on inhomogeneous LGCPs, second-order intensity-reweighted stationarity is not satisfied and the usual two step procedure for parameter estimation based on e.g. composite likelihood does not easily apply. Instead we propose a fast three step procedure based on composite likelihood. We apply our modelling and estimation framework

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

<a href="/en/project/GA19-04412S" target="_blank" >GA19-04412S: New approaches to modeling and statistics of random sets</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Spatial Statistics

ISSN

2211-6753

e-ISSN

—

Volume of the periodical

2019

Issue of the periodical within the volume

33

Country of publishing house

GB - UNITED KINGDOM

Number of pages

23

Pages from-to

1-23

UT code for WoS article

000489753700003

EID of the result in the Scopus database

2-s2.0-85072734675