Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10287201" target="_blank" >RIV/00216208:11320/14:10287201 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1111/stan.12030" target="_blank" >http://dx.doi.org/10.1111/stan.12030</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/stan.12030" target="_blank" >10.1111/stan.12030</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity

Popis výsledku v původním jazyce

Recent development of intensity estimation for inhomogeneous spatial point processes with covariates suggests that kerneling in the covariate space is a competitive intensity estimation method for inhomogeneous Poisson processes. It is not known whetherthis advantageous performance is still valid when the points interact. In the simplest common case, this happens, for example, when the objects presented as points have a spatial dimension. In this paper, kerneling in the covariate space is extended to Gibbs processes with covariates-dependent chemical activity and inhibitive interactions, and the performance of the approach is studied through extensive simulation experiments. It is demonstrated that under mild assumptions on the dependence of the intensity on covariates, this approach can provide better results than the classical nonparametric method based on local smoothing in the spatial domain. In comparison with the parametric pseudo-likelihood estimation, the nonparametric approac

Název v anglickém jazyce

Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity

Popis výsledku anglicky

Recent development of intensity estimation for inhomogeneous spatial point processes with covariates suggests that kerneling in the covariate space is a competitive intensity estimation method for inhomogeneous Poisson processes. It is not known whetherthis advantageous performance is still valid when the points interact. In the simplest common case, this happens, for example, when the objects presented as points have a spatial dimension. In this paper, kerneling in the covariate space is extended to Gibbs processes with covariates-dependent chemical activity and inhibitive interactions, and the performance of the approach is studied through extensive simulation experiments. It is demonstrated that under mild assumptions on the dependence of the intensity on covariates, this approach can provide better results than the classical nonparametric method based on local smoothing in the spatial domain. In comparison with the parametric pseudo-likelihood estimation, the nonparametric approac

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP201%2F10%2F0472" target="_blank" >GAP201/10/0472: Stochastická geometrie - nehomogenita, kótování, dynamika a stereologie</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Statistica Neerlandica

ISSN

0039-0402

e-ISSN

—

Svazek periodika

68

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

25

Strana od-do

225-249

Kód UT WoS článku

000340585200004

EID výsledku v databázi Scopus

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