Modelagem estatística de extremos espaciais com base em processos max-stable aplicados a dados meteorológicos no estado do Paraná / Statistical modelling of spatial extremes based on max-stable processes applied to environmental data in the Parana State

AUTOR(ES)

Ricardo Alves de Olinda

FONTE

IBICT - Instituto Brasileiro de Informação em Ciência e Tecnologia

DATA DE PUBLICAÇÃO

09/08/2012

RESUMO

The most mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well-developed, the extension of these tools to model spatial extremes data is currently a very active area of research. Modeling of maximum values under the spatial domain, applied to meteorological data is important for the proper management of risks and environmental disasters in the countries where the agricultural sector has great influence on the economy. A natural approach for such modeling is the theory of extreme spatial and max-stable process, characterized by infinite dimensional extension of multivariate extreme value theory, and we can then incorporate the current correlation functions in geostatistics and thus, check the extreme dependence through the extreme coefficient and the madogram. This thesis describes the application of such procedures in the modeling of spatial maximum dependency of monthly maximum rainfall of Paraná State, historical series based on observed meteorological stations. The proposed models consider the Euclidean space and a transformation called climatic space, which makes it possible to explain the presence of directional effects resulting from synoptic weather patterns. This methodology is based on the theorem proposed by De Haan (1984) and Smith (1990) models and Schlather (2002), checking the isotropic and anisotropic behavior these models through Monte Carlo simulation. Estimates are performed using maximum pairwise likelihood and the models are compared using the Takeuchi information criterion. The algorithm used in the fit is very fast and robust, allowing a good statistical and computational efficiency in monthly maximum rainfall modeling, allowing the modeling of directional effects resulting from environmental phenomena.

ASSUNTO(S)

geoestatística anisotropy climatic space espaço climático extremal coefficient geostatistics madogram método de monte carlo modelagem de dados monte carlo simulation pairwise likelihood simulação - estatística verossimilhança

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