Inference for cluster point processes with over- or under-dispersed cluster sizes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12510%2F20%3A43900922" target="_blank" >RIV/60076658:12510/20:43900922 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11222-020-09960-8?wt_mc=Internal.Event.1.SEM.ArticleAuthorAssignedToIssue" target="_blank" >https://link.springer.com/article/10.1007/s11222-020-09960-8?wt_mc=Internal.Event.1.SEM.ArticleAuthorAssignedToIssue</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11222-020-09960-8" target="_blank" >10.1007/s11222-020-09960-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Inference for cluster point processes with over- or under-dispersed cluster sizes

Popis výsledku v původním jazyce

Cluster point processes comprise a class of models that have been used for a wide range of applications. While several models have been studied for the probability density function of the offspring displacements and the parent point process, there are few examples of non-Poisson distributed cluster sizes. In this paper, we introduce a generalization of the Thomas process, which allows for the cluster sizes to have a variance that is greater or less than the expected value. We refer to this as the cluster sizes being over- and under-dispersed, respectively. To fit the model, we introduce minimum contrast methods and a Bayesian MCMC algorithm. These are evaluated in a simulation study. It is found that using the Bayesian MCMC method, we are in most cases able to detect over- and under-dispersion in the cluster sizes. We use the MCMC method to fit the model to nerve fiber data, and contrast the results to those of a fitted Thomas process

Název v anglickém jazyce

Inference for cluster point processes with over- or under-dispersed cluster sizes

Popis výsledku anglicky

Cluster point processes comprise a class of models that have been used for a wide range of applications. While several models have been studied for the probability density function of the offspring displacements and the parent point process, there are few examples of non-Poisson distributed cluster sizes. In this paper, we introduce a generalization of the Thomas process, which allows for the cluster sizes to have a variance that is greater or less than the expected value. We refer to this as the cluster sizes being over- and under-dispersed, respectively. To fit the model, we introduce minimum contrast methods and a Bayesian MCMC algorithm. These are evaluated in a simulation study. It is found that using the Bayesian MCMC method, we are in most cases able to detect over- and under-dispersion in the cluster sizes. We use the MCMC method to fit the model to nerve fiber data, and contrast the results to those of a fitted Thomas process

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/GA19-04412S" target="_blank" >GA19-04412S: Nové přístupy k modelování a statistice náhodných množin</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Statistics and Computing

ISSN

0960-3174

e-ISSN

—

Svazek periodika

30

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

1573-1590

Kód UT WoS článku

000548474400001

EID výsledku v databázi Scopus

2-s2.0-85087963812