On parameter estimation for doubly inhomogeneous cluster point processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F17%3A43892131" target="_blank" >RIV/60076658:12510/17:43892131 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.spasta.2017.03.005" target="_blank" >http://dx.doi.org/10.1016/j.spasta.2017.03.005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.spasta.2017.03.005" target="_blank" >10.1016/j.spasta.2017.03.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On parameter estimation for doubly inhomogeneous cluster point processes

Popis výsledku v původním jazyce

Nowadays, spatial inhomogeneity and clustering are two important features frequently observed in point patterns. These features often reveal heterogeneity of processes/factors involved in the point pattern formation and interaction determining the relative locations of points. Thus, inhomogeneous cluster point processes can be viewed as flexible and relevant models for describing point patterns observed in biology, forestry and economics for example. In this article, we consider cluster point processes with double inhomogeneity in which locations of cluster centers are drawn under an inhomogeneous parametric intensity function and the distribution of clusters is spatially inhomogeneous and depends on a given parametric function. We propose a Bayesian estimation procedure based on an MCMC algorithm to simultaneously estimate inhomogeneity parameters, cluster parameters and cluster centers. This modeling and estimation framework was applied to a toy case study dealing with the small-scale dispersal of spores of a fungal pathogen infecting plants.

Název v anglickém jazyce

On parameter estimation for doubly inhomogeneous cluster point processes

Popis výsledku anglicky

Nowadays, spatial inhomogeneity and clustering are two important features frequently observed in point patterns. These features often reveal heterogeneity of processes/factors involved in the point pattern formation and interaction determining the relative locations of points. Thus, inhomogeneous cluster point processes can be viewed as flexible and relevant models for describing point patterns observed in biology, forestry and economics for example. In this article, we consider cluster point processes with double inhomogeneity in which locations of cluster centers are drawn under an inhomogeneous parametric intensity function and the distribution of clusters is spatially inhomogeneous and depends on a given parametric function. We propose a Bayesian estimation procedure based on an MCMC algorithm to simultaneously estimate inhomogeneity parameters, cluster parameters and cluster centers. This modeling and estimation framework was applied to a toy case study dealing with the small-scale dispersal of spores of a fungal pathogen infecting plants.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA16-03708S" target="_blank" >GA16-03708S: Prostorová geometrická statistika náhodných množin v eukleidovských prostorech</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Spatial Statistics

ISSN

2211-6753

e-ISSN

—

Svazek periodika

2017

Číslo periodika v rámci svazku

20

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

191-205

Kód UT WoS článku

000405608800009

EID výsledku v databázi Scopus

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