About: Parameter Estimation of Spatial Poisson Process in Domain with Unknown Boundary

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Parameter Estimation of Spatial Poisson Process in Domain with Unknown Boundary Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	A spatial Poisson process is used to model a number of events in a domain. Often, we need to estimate an intensity of event occurrence to determine whether there is some risk (extinction, malfunction, harmfulness) due to the presence or absence of this event. The easiest way (for estimation) to obtain the intensity is to examine the whole domain and make as many repetitions as possible. However, in many cases it is expensive or even impossible to examine the whole domain or even to make repetitions. Therefore, we have to work with a sample domain that has no natural boundary and we cannot make repetitions. In many cases we do not have any information of the boundary of the domain (e.g. in records there is no information about start of monitoring) or this boundary does not exists. In this paper, the only information we are working with in estimation of the parameter λ are coordinates of each event (e.g. obtained by GPS technology). There is proposed unbiased estimator and this estimator is test A spatial Poisson process is used to model a number of events in a domain. Often, we need to estimate an intensity of event occurrence to determine whether there is some risk (extinction, malfunction, harmfulness) due to the presence or absence of this event. The easiest way (for estimation) to obtain the intensity is to examine the whole domain and make as many repetitions as possible. However, in many cases it is expensive or even impossible to examine the whole domain or even to make repetitions. Therefore, we have to work with a sample domain that has no natural boundary and we cannot make repetitions. In many cases we do not have any information of the boundary of the domain (e.g. in records there is no information about start of monitoring) or this boundary does not exists. In this paper, the only information we are working with in estimation of the parameter λ are coordinates of each event (e.g. obtained by GPS technology). There is proposed unbiased estimator and this estimator is test (en)
Title	Parameter Estimation of Spatial Poisson Process in Domain with Unknown Boundary Parameter Estimation of Spatial Poisson Process in Domain with Unknown Boundary (en)
skos:prefLabel	Parameter Estimation of Spatial Poisson Process in Domain with Unknown Boundary Parameter Estimation of Spatial Poisson Process in Domain with Unknown Boundary (en)
skos:notation	RIV/49777513:23520/10:00504048!RIV11-MSM-23520___
http://linked.open...avai/riv/aktivita	S Z
http://linked.open...avai/riv/aktivity	S, Z(MSM4977751301)
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Marek, Patrice Ťoupal, Tomáš
http://linked.open.../riv/druhVysledku	O - Ostatní výsledky nezařaditelné do žádného z výše uvedených druhů výsledku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Západočeská univerzita v Plzni / Fakulta aplikovaných věd
http://linked.open...dnocenehoVysledku	278070
http://linked.open...ai/riv/idVysledku	RIV/49777513:23520/10:00504048
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Spatial Poisson process; parameter estimation; unknown boundary; separating domain; risk (en)
http://linked.open.../riv/klicoveSlovo	parameter estimation risk Spatial Poisson process separating domain unknown boundary
http://linked.open...ontrolniKodProRIV	[55D214217198]
http://linked.open...in/vavai/riv/obor	BB
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2010
http://linked.open...iv/tvurceVysledku	Marek, Patrice Ťoupal, Tomáš
http://linked.open...n/vavai/riv/zamer	Spojité a diskrétní matematické struktury a vývoj odpovídajících metod jejich zkoumání
http://localhost/t...ganizacniJednotka	23520

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software