A note on analysis of extreme minimum temperatures with the GAMLSS framework
Abstract Estimation of return levels, based on extreme value distributions, is of importance in the earth and environmental sciences. To incorporate non-stationarity in the modelling, the statistical framework of generalised additive models for location, scale and shape is an option, providing flexi...
| Published in: | Acta Geophysica |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
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Springer Science and Business Media LLC
2019
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| Online Access: | http://dx.doi.org/10.1007/s11600-019-00363-6 http://link.springer.com/content/pdf/10.1007/s11600-019-00363-6.pdf http://link.springer.com/article/10.1007/s11600-019-00363-6/fulltext.html |
| Summary: | Abstract Estimation of return levels, based on extreme value distributions, is of importance in the earth and environmental sciences. To incorporate non-stationarity in the modelling, the statistical framework of generalised additive models for location, scale and shape is an option, providing flexibility and with a wide range of distributions implemented. With a large set of selections possible, model choice is an issue. As a case study, we investigate annual minimum temperatures from measurements at a location in northern Sweden. For practical work, it turns out that care must be taken in examining the obtained distributions, not solely relying on information criteria. A simulation study illustrates the findings. |
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