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Statements

Subject Item
n2:RIV%2F60076658%3A12510%2F14%3A43886793%21RIV15-GA0-12510___
rdf:type
n16:Vysledek skos:Concept
dcterms:description
This paper is concerned with parameter estimation for the Neyman-Scott point process with inhomogeneous cluster centers. Inhomogeneity depends on spatial covariates. The regression parameters are estimated at the first step using a Poisson likelihood score function. Three estimation procedures (minimum contrast method based on a modified K function, composite likelihood and Bayesian methods) are introduced for estimation of clustering parameters at the second step. The performance of the estimation methods are studied and compared via a simulation study. This work has been motivated and illustrated by ecological studies of fish spatial distribution in an inland reservoir. This paper is concerned with parameter estimation for the Neyman-Scott point process with inhomogeneous cluster centers. Inhomogeneity depends on spatial covariates. The regression parameters are estimated at the first step using a Poisson likelihood score function. Three estimation procedures (minimum contrast method based on a modified K function, composite likelihood and Bayesian methods) are introduced for estimation of clustering parameters at the second step. The performance of the estimation methods are studied and compared via a simulation study. This work has been motivated and illustrated by ecological studies of fish spatial distribution in an inland reservoir.
dcterms:title
Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers
skos:prefLabel
Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers
skos:notation
RIV/60076658:12510/14:43886793!RIV15-GA0-12510___
n3:aktivita
n13:P
n3:aktivity
P(GAP201/10/0472)
n3:cisloPeriodika
1
n3:dodaniDat
n11:2015
n3:domaciTvurceVysledku
n9:9147683
n3:druhVysledku
n15:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
51520
n3:idVysledku
RIV/60076658:12510/14:43886793
n3:jazykVysledku
n17:eng
n3:klicovaSlova
Neyman-Scott point process; Modified K function; Minimum contrast method; Inhomogeneous point process; Inhomogeneous cluster centers; Clustering; Composite likelihood; Bayesian method
n3:klicoveSlovo
n5:Modified%20K%20function n5:Bayesian%20method n5:Neyman-Scott%20point%20process n5:Composite%20likelihood n5:Minimum%20contrast%20method n5:Inhomogeneous%20cluster%20centers n5:Inhomogeneous%20point%20process n5:Clustering
n3:kodStatuVydavatele
NL - Nizozemsko
n3:kontrolniKodProRIV
[8AA92F400B12]
n3:nazevZdroje
Statistic and Computing
n3:obor
n14:BB
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:projekt
n10:GAP201%2F10%2F0472
n3:rokUplatneniVysledku
n11:2014
n3:svazekPeriodika
24
n3:tvurceVysledku
Mrkvička, Tomáš Muška, Milan Kubečka, Jan
n3:wos
000329246300007
s:issn
0960-3174
s:numberOfPages
10
n19:doi
10.1007/s11222-012-9355-3
n18:organizacniJednotka
12510