Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10687-007-0032-4
DC FieldValue
dc.titleVector generalized linear and additive extreme value models
dc.contributor.authorYee, T.W.
dc.contributor.authorStephenson, A.G.
dc.date.accessioned2014-10-28T05:16:31Z
dc.date.available2014-10-28T05:16:31Z
dc.date.issued2007-06
dc.identifier.citationYee, T.W.,Stephenson, A.G. (2007-06). Vector generalized linear and additive extreme value models. Extremes 10 (1-2) : 1-19. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/s10687-007-0032-4" target="_blank">https://doi.org/10.1007/s10687-007-0032-4</a>
dc.identifier.issn13861999
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105462
dc.description.abstractOver recent years parametric and nonparametric regression has slowly been adopted into extreme value data analysis. Its introduction has been characterized by piecemeal additions and embellishments, which has had a negative effect on software development and usage. The purpose of this article is to convey the classes of vector generalized linear and additive models (VGLMs and VGAMs) as offering significant advantages for extreme value data analysis, providing flexible smoothing within a unifying framework. In particular, VGLMs and VGAMs allow all parameters of extreme value distributions to be modelled as linear or smooth functions of covariates. We implement new auxiliary methodology by incorporating a quasi-Newton update for the working weight matrices within an iteratively reweighted least squares (IRLS) algorithm. A software implementation by the first author, called the vgam package for [InlineMediaObject not available: see fulltext.], is used to illustrate the potential of VGLMs and VGAMs. © 2007 Springer Science+Business Media, LLC.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s10687-007-0032-4
dc.sourceScopus
dc.subjectExtreme value modelling
dc.subjectFisher scoring
dc.subjectIteratively reweighted least squares
dc.subjectMaximum likelihood estimation
dc.subjectPenalized likelihood
dc.subjectSmoothing
dc.subjectVector splines
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1007/s10687-007-0032-4
dc.description.sourcetitleExtremes
dc.description.volume10
dc.description.issue1-2
dc.description.page1-19
dc.identifier.isiutNOT_IN_WOS
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