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Convidamos a comunidade para o Ciclo de Palestras do Programa de Pós-Graduação em Matemática Aplicada e Computacional - FCT/Unesp. Palestrante:
Profa.
Dra. Thaís Cristina Oliveira da Fonseca (IM/UFRJ) Data: 18
de Junho de 2011 Resumo: The aim of this work is to construct a general class of covariance
functions for processes that vary
continuously in space and time. Stochastic modeling of phenomena over space and time is
important in many areas of application. But choice of an appropriate model can be
difficult as one must take care to use valid covariance structures. A common
choice for the process is a product of purely spatial and temporal random
processes. In this case, the resulting process possesses a separable
covariance function. Although these models are guaranteed to be valid, they are
severely limited, since they do not allow space-time interactions. We propose a
general and flexible way of constructing covariance functions derived through
mixing over separable covariance functions. We illustrate our modeling approach
in the Irish wind data and present
some extensions. ================================================ Aparecida D. P. Souza
Departamento de Matemática, Estatística e Computação Faculdade de Ciências e Tecnologia UNESP - Campus de Presidente Prudente-SP Fone: + 55 18 3229-5617 - Fax: + 55 18 3221-8333 ================================================ |