Dependence modelling for spatial extremes
Current dependence models for spatial extremes are based upon max-stable processes. Within this class, there are few inferentially viable models available, and we propose one further model. More problematic are the restrictive assumptions that must be made when using max-stable processes to model de...
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| Veröffentlicht in: | Biometrika 2012-06, Vol.99 (2), p.253-272 |
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| container_title | Biometrika |
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| creator | WADSWORTH, JENNIFER L. TAWN, JONATHAN A. |
| description | Current dependence models for spatial extremes are based upon max-stable processes. Within this class, there are few inferentially viable models available, and we propose one further model. More problematic are the restrictive assumptions that must be made when using max-stable processes to model dependence for spatial extremes: it must be assumed that the dependence structure of the observed extremes is compatible with a limiting model that holds for all events more extreme than those that have already occurred. This problem has long been acknowledged in the context of finite-dimensional multivariate extremes, in particular when data display dependence at observable levels, but are independent in the limit. We propose a flexible class of models that is suitable for such data in a spatial context. In addition, we consider the situation where the extremal dependence structure may vary with distance. We apply our models to spatially referenced significant wave height data from the North Sea, finding evidence that their extremal structure is not compatible with a limiting dependence model. |
| doi_str_mv | 10.1093/biomet/asr080 |
| format | Article |
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Within this class, there are few inferentially viable models available, and we propose one further model. More problematic are the restrictive assumptions that must be made when using max-stable processes to model dependence for spatial extremes: it must be assumed that the dependence structure of the observed extremes is compatible with a limiting model that holds for all events more extreme than those that have already occurred. This problem has long been acknowledged in the context of finite-dimensional multivariate extremes, in particular when data display dependence at observable levels, but are independent in the limit. We propose a flexible class of models that is suitable for such data in a spatial context. In addition, we consider the situation where the extremal dependence structure may vary with distance. 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Global analysis and analysis on manifolds ; Waves</subject><ispartof>Biometrika, 2012-06, Vol.99 (2), p.253-272</ispartof><rights>2012 Biometrika Trust</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Oxford Publishing Limited(England) Jun 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c350t-233d852f560525ea40b255e386fcba923b276147de5e9bd36fec31affa2107583</citedby><cites>FETCH-LOGICAL-c350t-233d852f560525ea40b255e386fcba923b276147de5e9bd36fec31affa2107583</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/41720690$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/41720690$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,776,780,799,828,27903,27904,57996,58000,58229,58233</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25911585$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>WADSWORTH, JENNIFER L.</creatorcontrib><creatorcontrib>TAWN, JONATHAN A.</creatorcontrib><title>Dependence modelling for spatial extremes</title><title>Biometrika</title><description>Current dependence models for spatial extremes are based upon max-stable processes. Within this class, there are few inferentially viable models available, and we propose one further model. More problematic are the restrictive assumptions that must be made when using max-stable processes to model dependence for spatial extremes: it must be assumed that the dependence structure of the observed extremes is compatible with a limiting model that holds for all events more extreme than those that have already occurred. This problem has long been acknowledged in the context of finite-dimensional multivariate extremes, in particular when data display dependence at observable levels, but are independent in the limit. We propose a flexible class of models that is suitable for such data in a spatial context. In addition, we consider the situation where the extremal dependence structure may vary with distance. We apply our models to spatially referenced significant wave height data from the North Sea, finding evidence that their extremal structure is not compatible with a limiting dependence model.</description><subject>Applications</subject><subject>Biology, psychology, social sciences</subject><subject>Conditional probabilities</subject><subject>Data processing</subject><subject>Distribution functions</subject><subject>Exact sciences and technology</subject><subject>General topics</subject><subject>Global analysis, analysis on manifolds</subject><subject>Inference</subject><subject>Mathematical functions</subject><subject>Mathematical independent variables</subject><subject>Mathematical maxima</subject><subject>Mathematical models</subject><subject>Mathematical problems</subject><subject>Mathematics</subject><subject>Multivariate analysis</subject><subject>Parametric models</subject><subject>Probability and statistics</subject><subject>Probability theory and stochastic processes</subject><subject>Sciences and techniques of general use</subject><subject>Spatial models</subject><subject>Statistical models</subject><subject>Statistics</subject><subject>Stochastic processes</subject><subject>Studies</subject><subject>Topology. 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Global analysis and analysis on manifolds</subject><subject>Waves</subject><issn>0006-3444</issn><issn>1464-3510</issn><issn>1464-3510</issn><issn>0006-3444</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNpdkE1LxDAQhoMouK4ePQoFEfRQN5Nk0vYo6ycseNFzSdOJdGmbNemC_nu7dNmDp2GYZ2ZeHsYugd8DL-SianxHw8LEwHN-xGagtEolAj9mM865TqVS6pSdxbjetRr1jN090ob6mnpLSedratum_0qcD0ncmKExbUI_Q6CO4jk7caaNdLGvc_b5_PSxfE1X7y9vy4dVaiXyIRVS1jkKh5qjQDKKVwKRZK6drUwhZCUyDSqrCamoaqkdWQnGOSOAZ5jLObud7m6C_95SHMquiXYMZnry21gCZJBnulA4otf_0LXfhn5MVwKHTCOq0cucpRNlg48xkCs3oelM-B2hcieunMSVk7iRv9lfNdGa1gXT2yYelgQWAJjvvl9N3DoOPhzmCjLBdcHlH04Vd4c</recordid><startdate>20120601</startdate><enddate>20120601</enddate><creator>WADSWORTH, JENNIFER L.</creator><creator>TAWN, JONATHAN A.</creator><general>Biometrika Trust, University College London</general><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20120601</creationdate><title>Dependence modelling for spatial extremes</title><author>WADSWORTH, JENNIFER L. ; TAWN, JONATHAN A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c350t-233d852f560525ea40b255e386fcba923b276147de5e9bd36fec31affa2107583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Applications</topic><topic>Biology, psychology, social sciences</topic><topic>Conditional probabilities</topic><topic>Data processing</topic><topic>Distribution functions</topic><topic>Exact sciences and technology</topic><topic>General topics</topic><topic>Global analysis, analysis on manifolds</topic><topic>Inference</topic><topic>Mathematical functions</topic><topic>Mathematical independent variables</topic><topic>Mathematical maxima</topic><topic>Mathematical models</topic><topic>Mathematical problems</topic><topic>Mathematics</topic><topic>Multivariate analysis</topic><topic>Parametric models</topic><topic>Probability and statistics</topic><topic>Probability theory and stochastic processes</topic><topic>Sciences and techniques of general use</topic><topic>Spatial models</topic><topic>Statistical models</topic><topic>Statistics</topic><topic>Stochastic processes</topic><topic>Studies</topic><topic>Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds</topic><topic>Waves</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>WADSWORTH, JENNIFER L.</creatorcontrib><creatorcontrib>TAWN, JONATHAN A.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Biometrika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>WADSWORTH, JENNIFER L.</au><au>TAWN, JONATHAN A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dependence modelling for spatial extremes</atitle><jtitle>Biometrika</jtitle><date>2012-06-01</date><risdate>2012</risdate><volume>99</volume><issue>2</issue><spage>253</spage><epage>272</epage><pages>253-272</pages><issn>0006-3444</issn><issn>1464-3510</issn><eissn>1464-3510</eissn><eissn>0006-3444</eissn><coden>BIOKAX</coden><abstract>Current dependence models for spatial extremes are based upon max-stable processes. Within this class, there are few inferentially viable models available, and we propose one further model. More problematic are the restrictive assumptions that must be made when using max-stable processes to model dependence for spatial extremes: it must be assumed that the dependence structure of the observed extremes is compatible with a limiting model that holds for all events more extreme than those that have already occurred. This problem has long been acknowledged in the context of finite-dimensional multivariate extremes, in particular when data display dependence at observable levels, but are independent in the limit. We propose a flexible class of models that is suitable for such data in a spatial context. In addition, we consider the situation where the extremal dependence structure may vary with distance. We apply our models to spatially referenced significant wave height data from the North Sea, finding evidence that their extremal structure is not compatible with a limiting dependence model.</abstract><cop>Oxford</cop><pub>Biometrika Trust, University College London</pub><doi>10.1093/biomet/asr080</doi><tpages>20</tpages></addata></record> |
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| source | Jstor Complete Legacy; Alma/SFX Local Collection; JSTOR; Oxford Journals |
| subjects | Applications Biology, psychology, social sciences Conditional probabilities Data processing Distribution functions Exact sciences and technology General topics Global analysis, analysis on manifolds Inference Mathematical functions Mathematical independent variables Mathematical maxima Mathematical models Mathematical problems Mathematics Multivariate analysis Parametric models Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use Spatial models Statistical models Statistics Stochastic processes Studies Topology. Manifolds and cell complexes. Global analysis and analysis on manifolds Waves |
| title | Dependence modelling for spatial extremes |
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