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| Titel |
Nonstationary risk analysis of climate extremes |
| VerfasserIn |
V. Chavez-Demoulin, A. C. Davison, M. Suveges |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250025159
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| Zusammenfassung |
| There is growing interest in the modelling of the size and frequency of rare events in a
changing climate. Standard models for extreme events are based on the modelling of annual
maxima or exceedances over high or under low thresholds: in either case appropriate
probability distributions are fitted to the data, and extrapolation to rare events is based on the
fitted models. Very often, however, extremal models do not take full advantage of techniques
that are standard in other domains of statistics.
Smoothing methods are now well-established in many domains of statistics, and are
increasingly used in analysis of extremal data. The crucial idea of smoothing is to replace a
simple linear or quadratic form of dependence of one variable on another by a more flexible
form, and thus to ‘allow the data to speak for themselves,ánd thus, perhaps, to reveal
unexpected features. There are many approaches to smoothing in the context of linear
regression, of which the use of spline smoothing and of local polynomial modelling are
perhaps the most common. Under the first, a basis of spline functions is used to represent the
dependence; often this is called generalised additive modelling. Under the second,
polynomial models are fitted locally to the data, resulting in a more flexible overall fit. The
selection of the degree of smoothing is crucial, and there are automatic ways to do
this.
The talk will describe some applications of smoothing to data on temperature extremes,
elucidating the relation between cold winter weather in the Alps and the North Atlantic
Oscillation, and changes in the lengths of usually hot and cold spells in Britain. The work
mixes classical models for extremes, generalised additive modelling, local polynomial
smoothing, and the bootstrap.
References
Chavez-Demoulin, V. and Davison, A. C. (2005) Generalized additive modelling of sample
extremes. Applied Statistics, 54, 207–222.
Süveges, M. (2007) Likelihood estimation of the extremal index. Extremes, 10,
41–55. |
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