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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/81782
- Vector generalized linear and additive extreme value models
- Yee, Thomas W.; Stephenson, Alec G.
- 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 r, is used to illustrate the potential of VGLMs and VGAMs.
- Publication type
- Journal article
- Extremes, Vol. 10, no. 1 (Jun 2007), pp. 1-19
- Publication year
- FOR Code(s)
- 01 Mathematical Sciences
- Extreme value modelling; Fisher scoring; Iteratively reweighted least squares; Maximum likelihood estimation; Penalized likelihood; Smoothing; Vector splines
- Publisher URL
- Copyright © 2007 Springer Science + Business Media, LLC.
- Peer reviewed