A seasonal-Markov model for extremely low temperatures.

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A seasonal-Markov model for extremely low temperatures. / Smith, R. L.; Tawn, J. A.; Coles, S. G.
In: Environmetrics, Vol. 5, No. 3, 1994, p. 221-239.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Smith, RL, Tawn, JA & Coles, SG 1994, 'A seasonal-Markov model for extremely low temperatures.', Environmetrics, vol. 5, no. 3, pp. 221-239. https://doi.org/10.1002/env.3170050304

APA

Smith, R. L., Tawn, J. A., & Coles, S. G. (1994). A seasonal-Markov model for extremely low temperatures. Environmetrics, 5(3), 221-239. https://doi.org/10.1002/env.3170050304

Vancouver

Smith RL, Tawn JA, Coles SG. A seasonal-Markov model for extremely low temperatures. Environmetrics. 1994;5(3):221-239. doi: 10.1002/env.3170050304

Author

Smith, R. L. ; Tawn, J. A. ; Coles, S. G. / A seasonal-Markov model for extremely low temperatures. In: Environmetrics. 1994 ; Vol. 5, No. 3. pp. 221-239.

Bibtex

@article{f9c1c386f4684be2883cfa6f8d60093d,

title = "A seasonal-Markov model for extremely low temperatures.",

abstract = "Time series of temperatures during periods of extreme cold display long-term seasonal variability and short-term temporal dependence. Classical approaches to extremes circumvent these issues, but in so doing cannot address questions relating to the temporal character of the process, though these issues are often the most important. In this paper a model is developed with the following features: periodic seasonal effects; consistency with asymptotic extreme value theory; Markov description of temporal dependence. Smith et al. studied the properties of such a model in the stationary case. Here, it is shown how such a model can be fitted to a non-stationary series, and consequently used to estimate temporal aspects of the extremal process of low temperatures which have most practical and scientific relevance.",

keywords = "Bivariate extreme value distribution • Generalized extreme value distribution • Generalized Pareto distribution • Markov process • Temperature",

author = "Smith, {R. L.} and Tawn, {J. A.} and Coles, {S. G.}",

year = "1994",

doi = "10.1002/env.3170050304",

language = "English",

volume = "5",

pages = "221--239",

journal = "Environmetrics",

issn = "1099-095X",

publisher = "John Wiley and Sons Ltd",

number = "3",

}

RIS

TY - JOUR

T1 - A seasonal-Markov model for extremely low temperatures.

AU - Smith, R. L.

AU - Tawn, J. A.

AU - Coles, S. G.

PY - 1994

Y1 - 1994

N2 - Time series of temperatures during periods of extreme cold display long-term seasonal variability and short-term temporal dependence. Classical approaches to extremes circumvent these issues, but in so doing cannot address questions relating to the temporal character of the process, though these issues are often the most important. In this paper a model is developed with the following features: periodic seasonal effects; consistency with asymptotic extreme value theory; Markov description of temporal dependence. Smith et al. studied the properties of such a model in the stationary case. Here, it is shown how such a model can be fitted to a non-stationary series, and consequently used to estimate temporal aspects of the extremal process of low temperatures which have most practical and scientific relevance.

AB - Time series of temperatures during periods of extreme cold display long-term seasonal variability and short-term temporal dependence. Classical approaches to extremes circumvent these issues, but in so doing cannot address questions relating to the temporal character of the process, though these issues are often the most important. In this paper a model is developed with the following features: periodic seasonal effects; consistency with asymptotic extreme value theory; Markov description of temporal dependence. Smith et al. studied the properties of such a model in the stationary case. Here, it is shown how such a model can be fitted to a non-stationary series, and consequently used to estimate temporal aspects of the extremal process of low temperatures which have most practical and scientific relevance.

KW - Bivariate extreme value distribution • Generalized extreme value distribution • Generalized Pareto distribution • Markov process • Temperature

U2 - 10.1002/env.3170050304

DO - 10.1002/env.3170050304

M3 - Journal article

VL - 5

SP - 221

EP - 239

JO - Environmetrics

JF - Environmetrics

SN - 1099-095X

IS - 3

ER -

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