To the top

Page Manager: Webmaster
Last update: 9/11/2012 3:13 PM

Tell a friend about this page
Print version

Inhomogeneous higher-orde… - University of Gothenburg, Sweden Till startsida
Sitemap
To content Read more about how we use cookies on gu.se

Inhomogeneous higher-order summary statistics for point processes on linear networks

Journal article
Authors Ottmar Cronie
Mehdi Moradi
Jorge Mateu
Published in Statistics and computing
ISSN 0960-3174
Publication year 2020
Published at Institute of Medicine, School of Public Health and Community Medicine
Language en
Links https://doi.org/10.1007/s11222-020-...
https://link.springer.com/article/1...
Keywords Inhomogeneous linear empty space function, Inhomogeneous linear J -function, Inhomogeneous linear nearest neighbour distance distribution function, Linear network, Pseudostationarity, Regular distance metric, Traffic accident data
Subject categories Probability Theory and Statistics, Mathematical statistics, Statistics

Abstract

As a workaround for the lack of transitive transformations on linear network structures, which are required to consider different notions of distributional invariance, including stationarity, we introduce the notions of pseudostationarity and intensity reweighted moment pseudostationarity for point processes on linear networks. Moreover, using arbitrary so-called regular linear network distances, e.g. the Euclidean and the shortest-path distance, we further propose geometrically corrected versions of different higher-order summary statistics, including the inhomogeneous empty space function, the inhomogeneous nearest neighbour distance distribution function and the inhomogeneous J-function. Such summary statistics detect interactions of order higher than two. We also discuss their nonparametric estimators and through a simulation study, considering models with different types of spatial interaction and different networks, we study the performance of our proposed summary statistics by means of envelopes. Our summary statistic estimators manage to capture clustering, regularity as well as Poisson process independence. Finally, we make use of our new summary statistics to analyse two different datasets: motor vehicle traffic accidents and spiderwebs.

Page Manager: Webmaster|Last update: 9/11/2012
Share:

The University of Gothenburg uses cookies to provide you with the best possible user experience. By continuing on this website, you approve of our use of cookies.  What are cookies?