untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:17:57Z</responseDate>
<request identifier=oai:HAL:hal-01502640v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-01502640v1</identifier>
<datestamp>2018-01-11</datestamp>
<setSpec>type:ART</setSpec>
<setSpec>subject:stat</setSpec>
<setSpec>collection:AGROPARISTECH</setSpec>
<setSpec>collection:CNRS</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:INRA</setSpec>
<setSpec>collection:CIRAD</setSpec>
<setSpec>collection:GUYANE</setSpec>
<setSpec>collection:ECOFOG</setSpec>
<setSpec>collection:MIA-PARIS</setSpec>
<setSpec>collection:AGROPARISTECH-ORG</setSpec>
<setSpec>collection:AGROPARISTECH-SIAFEE</setSpec>
<setSpec>collection:AGROPARISTECH-MMIP</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Testing randomness of spatial point patterns with the Ripley statistic</title>
<creator>Lang, Gabriel</creator>
<creator>Marcon, Eric</creator>
<contributor>Mathématiques et Informatique Appliquées (MIA-Paris) ; Institut National de la Recherche Agronomique (INRA) - AgroParisTech</contributor>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Université de Guyane (UG) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>ISSN: 1292-8100</source>
<source>EISSN: 1262-3318</source>
<source>ESAIM: Probability and Statistics</source>
<publisher>EDP Sciences</publisher>
<identifier>hal-01502640</identifier>
<identifier>https://hal-agroparistech.archives-ouvertes.fr/hal-01502640</identifier>
<source>https://hal-agroparistech.archives-ouvertes.fr/hal-01502640</source>
<source>ESAIM: Probability and Statistics, EDP Sciences, 2013, 17, pp.767 - 788. 〈10.1051/ps/2012027〉</source>
<identifier>DOI : 10.1051/ps/2012027</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1051/ps/2012027</relation>
<language>en</language>
<subject lang=en> Poisson process</subject>
<subject lang=en> null</subject>
<subject lang=en> point pattern</subject>
<subject lang=en>Central limit theorem</subject>
<subject lang=en> goodness-of-fit test</subject>
<subject lang=en> Höffding decomposition</subject>
<subject>[STAT] Statistics [stat]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en> Aggregation patterns are often visually detected in sets of location data. These clusters may be the result of interesting dynamics or the effect of pure randomness. We build an asymptotically Gaussian test for the hypothesis of randomness corresponding to a homogeneous Poisson point process. We first compute the exact first and second moment of the Ripley K-statistic under the homogeneous Poisson point process model. Then we prove the asymptotic normality of a vector of such statistics for different scales and compute its covariance matrix. From these results, we derive a test statistic that is chi-square distributed. By a Monte-Carlo study, we check that the test is numerically tractable even for large data sets and also correct when only a hundred of points are observed</description>
<date>2013-01-15</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
