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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Inference for cluster point processes with over- or under-dispersed cluster sizes
Original language description
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
Czech name
—
Czech description
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Type
J<sub>imp</sub> - 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
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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