NDLI: Time-varying models for extreme values

Content Provider	Springer Nature Link
Author	Huerta, Gabriel Sansó, Bru
Copyright Year	2007
Abstract	We propose a new approach for modeling extreme values that are measured in time and space. First we assume that the observations follow a Generalized Extreme Value (GEV) distribution for which the location, scale or shape parameters define the space–time structure. The temporal component is defined through a Dynamic Linear Model (DLM) or state space representation that allows to estimate the trend or seasonality of the data in time. The spatial element is imposed through the evolution matrix of the DLM where we adopt a process convolution form. We show how to produce temporal and spatial estimates of our model via customized Markov Chain Monte Carlo (MCMC) simulation. We illustrate our methodology with extreme values of ozone levels produced daily in the metropolitan area of Mexico City and with rainfall extremes measured at the Caribbean coast of Venezuela.
Starting Page	285
Ending Page	299
Page Count	15
File Format	PDF
ISSN	13528505
Journal	Environmental and Ecological Statistics
Volume Number	14
Issue Number	3
e-ISSN	15733009
Language	English
Publisher	Springer US
Publisher Date	2007-07-10
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Spatio-temporal process Extreme values GEV distribution Process convolutions MCMC Ozone levels Evolutionary Biology Mathematical Biology in General Statistics Ecology
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Environmental Science Statistics, Probability and Uncertainty

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