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dc.contributor.authorBardsley, W. Earlen_NZ
dc.contributor.authorVetrova, Varvaraen_NZ
dc.contributor.authorDao, Ngoc Hieu
dc.date.accessioned2019-01-04T01:38:47Z
dc.date.available2019en_NZ
dc.date.available2019-01-04T01:38:47Z
dc.date.issued2019en_NZ
dc.identifier.citationBardsley, W. E., Vetrova, V., & Dao, N. H. (2019). Line mesh distributions: An alternative approach for multivariate environmental extremes. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-018-1642-xen
dc.identifier.issn1436-3240en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12251
dc.description.abstractCopulas and other multivariate models can give joint exceedance probabilities for multivariate events in the naturalenvironment. However, the choice of the most appropriate multivariate model may not always be evident in the absence ofknowledge of dependence structures. A simple nonparametric alternative is to approximate multivariate dependenciesusing ‘‘line mesh distributions’’, introduced here as a data-based finite mixture of univariate distributions defined on a meshof L =C(m, 2) lines extending through Euclidean n-space. That is, m data points in n-space define a total of L lines, whereC() denotes the binomial coefficient. The utilitarian simplicity of the method has attraction for joint exceedance proba-bilities because just the data and a single bandwidth parameter within the 0, 1 interval are needed to define a line meshdistribution. All bivariate planes in these distributions have the same Pearson correlation coefficients as the correspondingdata. Marginal means and variances are similarly preserved. Using an example from the literature, a 5-parameter bivariateGumbel model is replaced with a 1-parameter line mesh distribution. A second illustration for three dimensions applies linemesh distributions to data simulated from a trivariate copula.
dc.language.isoen
dc.publisherSpringer (part of Springer Nature)en_NZ
dc.relation.urihttps://rdcu.be/be19Q
dc.subjectLine mesh distributionen_NZ
dc.subjectCopulaen_NZ
dc.subjectNonparametricen_NZ
dc.subjectMultivariateen_NZ
dc.subjectJoint exceedance estimationen_NZ
dc.subjectLine mesh distribution
dc.subjectCopula
dc.subjectNonparametric
dc.subjectMultivariate
dc.subjectJoint exceedance estimation
dc.titleLine mesh distributions: An alternative approach for multivariate environmental extremesen_NZ
dc.typeJournal Article
dc.identifier.doi10.1007/s00477-018-1642-xen_NZ
dc.relation.isPartOfStochastic Environmental Research and Risk Assessmenten_NZ
pubs.declined2019-01-04T14:29:19.234+1300
pubs.elements-id231091
pubs.publication-statusPublished onlineen_NZ


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