A note on analysis of extreme minimum temperatures with the GAMLSS framework

Rydén, Jesper

doi:10.1007/s11600-019-00363-6

Artykuł - szczegóły

Tytuł artykułu

A note on analysis of extreme minimum temperatures with the GAMLSS framework

Autorzy

Rydén Jesper

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-019-00363-6

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

GEV distribution GAMLSS non-stationary model extreme temperatures model selection

dystrybucja GEV GAMLSS model niestacjonarny temperatury ekstremalne wybór modelu

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2019

Tom

Vol. 67, no. 6

Strony

1599--1604

Opis fizyczny

Bibliogr. 37 poz.

Twórcy

autor

Rydén Jesper

jesper.ryden@slu.se

Swedish University of Agricultural Sciences, Uppsala, Sweden

Bibliografia

1. Agresti A (2015) Foundations of linear and generalized linear models. Wiley, Hoboken
2. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19:716–723. https://doi.org/10.1109/TAC.1974.1100705
3. Barlow KM, Christy BP, O’Leary GJ, Riffkin PA, Nuttall JG (2015) Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crops Res 171:109–119. https://doi.org/10.1016/j.fcr.2014.11.010
4. Bücher A, Segers J (2017) On the maximum likelihood estimator for the generalized extreme-value distribution. Extremes 20:839–872. https://doi.org/10.1007/s10687-017-0292-6
5. Caroni C, Panagoulia D (2016) Non-stationary modelling of extreme temperatures in a montainous area of Greece. REVSTAT Stat J 14:217–22
6. Cleveland WS, Devlin SJ (1988) Locally-weighted regression: an approach to regression analysis by local fitting. J Am Stat Assoc 83:596–610. https://doi.org/10.1080/01621459.1988.10478639
7. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London
8. Cooley D (2013) Return periods and return levels under climate change. In: AghaKouchak A et al. (ed.) Extremes in a changing climate. Detection, analysis and uncertainty. Springer, Berlin, pp 97–114. https://doi.org/10.1007/978-94-007-4479-0_4
9. Debele SE, Bogdanowicz E, Strupczewski WG (2017) Around and about an application of the GAMLSS package to non-stationary flood frequency analysis. Acta Geophys 65:885–892. https://doi.org/10.1007/s11600-017-0072-3
10. Dey DK, Roy D, Yan J (2016) Univariate extreme value analysis. In: Dey DK, Yan J (eds) Extreme value modeling and risk analysis. Methods and applications. CRC Press, Boca Raton, pp 1–22
11. Fernandez B, Salas JD (1999) Return period and risk of hydrologic events. I: mathematical foundation. J Hydrol Eng 4:297–307. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:4(297)
12. Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank AMG, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19:193–212. https://doi.org/10.3354/cr019193
13. Gilleland E, Ribatet M, Stephenson AG (2013) A software review for extreme value analysis. Extremes 16:103–119. https://doi.org/10.1007/s10687-012-0155-0
14. Gilleland E, Katz RW (2016) extRemes 2.0: an extreme value analysis package in R. J Stat Softw 72(8):1–39. https://doi.org/10.18637/jss.v072.i08
15. Gomes MI, Guillou A (2015) Extreme value theory and statistics of univariate extremes: a review. Int Stat Rev 83:263–292. https://doi.org/10.1111/insr.12058
16. Gumbel E (1958) Statistics of extremes. Columbia University Press, New York
17. Hilbe JM (2011) Negative binomial regression, 2nd edn. Cambridge University Press, Cambridge
18. Klein Tank AMG, Wijngaard JB, Können GP, Böhm R, Demarée G, Gocheva A, Mileta M, Pashiardis S, Hejkrlik L, Kern-Hansen C, Heino R, Bessemoulin P, Müller-Westermeier G, Tzanakou M, Szalai S, Pálsdóttir T, Fitzgerald D, Rubin S, Capaldo M, Maugeri M, Leitass A, Bukantis A, Aberfeld R, van Engelen AFV, Forland E, Mietus M, Coelho F, Mares C, Razuvaev V, Nieplova E, Cegnar T, Antonio López J, Dahlström B, Moberg A, Kirchhofer W, Ceylan A, Pachaliuk O, Alexander LV, Petrovic P (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European climate assessment. Int J Climatol 22(12):1441–1453. https://doi.org/10.1002/joc.773
19. Koutsoyiannis D, Montanari A (2015) Negligent killing of scientific concepts: the stationarity case. Hydrolog Sci J 60(7–8):1174–1183. https://doi.org/10.1080/02626667.2014.959959
20. Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319:573–574. https://doi.org/10.1126/science.1151915
21. Naghettini M (2017) Fundamentals of statistical hydrology (ed.). Springer, Berlin
22. Nelder JA, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc Ser A 135:370–384. https://doi.org/10.2307/2344614
23. Obeysekera J, Salas JD (2014) Quantifying the uncertainty of design floods under nonstationary conditions. J Hydrol Eng 19(7):1438–1446. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000931
24. Panagoulia D, Economou P, Caroni C (2014) Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a montainous area under climate change. Environmetrics 25:29–43. https://doi.org/10.1002/env.2252
25. Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape. J R Stat Soc Ser C Appl Stat 54:507–554. https://doi.org/10.1111/j.1467-9876.2005.00510.x
26. Rootzén H, Katz RW (2013) Design life level: quantifying risk in a changing climate. Water Resour Res 49:5964–5972. https://doi.org/10.1002/wrcr.20425
27. Rychlik I, Rydén J (2006) Probability and risk analysis. an introduction for engineers. Springer, Berlin
28. Salas JD, Obeysekera J (2014) Revisiting the concepts of return period and risk for nonstationary hydrologic extreme events. J Hydrol Eng 19:554–568. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820
29. Schwarz GE (1978) Estimating the dimension of a model. Ann Stat 6:461–464. https://doi.org/10.1214/aos/1176344136
30. Serinaldi F, Kilsby CG (2015) Stationarity is undead: uncertainty dominates the distribution of extremes. Adv Water Resour 77:17–36. https://doi.org/10.1016/j.advwatres.2014.12.013
31. Smith RL (1985) Maximum likelihood estimation in a class of nonregular cases. Biometrika 72(1):67–90. https://doi.org/10.1093/biomet/72.1.67
32. Stasinopoulos DM, Rigby RA (2007) Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw 23(7)1:1–46. https://doi.org/10.18637/jss.v023.i07
33. Stasinopoulos M, Rigby B, Akantziliotou C (2008) Instructions on how to use the gamlss package in R. Second Edition. http://www.gamlss.com/wp-content/uploads/2013/01/gamlss-manual.pdf
34. Stasinopoulos M, Rigby R (2018). gamlss.dist: distributions for generalized additive models for location scale and shape. R package version 5.0-6. https://CRAN.R-project.org/package=gamlss.dist
35. Stephenson DB (2008) Definition, diagnosis, and origin of extreme weather and climate events. In: Diaz HF, Murnane RJ (eds) Climate extremes and society. Cambridge University Press, Cambridge
36. Villarini G, Smith JA, Napolitano F (2010) Nonstationary modelling of a long record of rainfall and temperature over Rome. Adv Water Resour 33:1256–1267. https://doi.org/10.1016/j.advwatres.2010.03.013
37. Zhang X, Zwiers FW (2013) Statistical indices for the diagnosing and detecting changes in extremes. In: AghaKouchak A et al. (ed.) Extremes in a changing climate. Detection, analysis and uncertainty. Springer, Berlin, pp 1–14. https://doi.org/10.1007/978-94-007-4479-0_1

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-a94ed2c2-f24b-4f1b-8905-56fcb8a7c950