Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10687-007-0032-4
Title: Vector generalized linear and additive extreme value models
Authors: Yee, T.W.
Stephenson, A.G. 
Keywords: Extreme value modelling
Fisher scoring
Iteratively reweighted least squares
Maximum likelihood estimation
Penalized likelihood
Smoothing
Vector splines
Issue Date: Jun-2007
Citation: Yee, T.W.,Stephenson, A.G. (2007-06). Vector generalized linear and additive extreme value models. Extremes 10 (1-2) : 1-19. ScholarBank@NUS Repository. https://doi.org/10.1007/s10687-007-0032-4
Abstract: Over 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.
Source Title: Extremes
URI: http://scholarbank.nus.edu.sg/handle/10635/105462
ISSN: 13861999
DOI: 10.1007/s10687-007-0032-4
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