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Regression Models for Exceedance Data via the Full LikelihoodFernando Ferraz do Nascimento ∗Universidade Federal do PiauíDani GamermanUniversidade Federal do Rio de JaneiroHedibert Freitas LopesUniversity of ChicagoMany situations in practice require appropriate specification of operating characteristics under extremeconditions. Typical examples include environmental sciences where studies include extreme temperature,rainfall and river flow to name a few. In these cases, the effect of geographic and climatologicalinputs are likely to play a relevant role. This paper is concerned with the study of extreme data inthe presence of relevant auxiliary information. The underlying model involves a mixture distribution:a generalized Pareto distribution is assumed for the exceedances beyond a high threshold and a nonparametricapproach is assumed for the data below the threshold. Thus, the full likelihood including databelow and above the threshold is considered in the estimation. The main novelty is the introduction of aregression structure to explain the variation of the exceedances through all tail parameters. Estimationis performed under the Bayesian paradigm and includes model choice. This allows for determination ofhigher quantiles under each covariate configuration and upper bounds for the data, where appropriate.Simulation results show that the models are appropriate and identifiable. The models are applied to thestudy of two temperature datasets: maxima in the U.S.A. and minima in Brazil, and compared to otherrelated models.∗ Apresentador/Speaker33
Spatially Varying Autoregressive ProcessesAline Araújo Nobre ∗PROCC/FIOCRUZBruno SansóUniversity of California at Santa CruzAlexandra M. SchmidtDME/<strong>UFRJ</strong>We develop a class of models for processes indexed in time and space that are based on autoregressive(AR) processes at each location. We use a Bayesian hierarchical structure to impose spatial coherencefor the coefficients of the AR processes. The priors on such coefficients consists of spatial processes thatguarantee time stationarity at each point in the spatial domain. The AR structures are coupled with adynamic model for the mean of the process, which is expressed as a linear combination of time-varyingparameters. We use satellite data on sea surface temperature for the North Pacific to illustrate how themodel can be used to separate trends, cycles and short term variability for high frequency environmentaldata.∗ Apresentador/Speaker34
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A Semiparametric Bayesian Approach
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Modelo Weibull Modificado de Longa
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LASSO Bayesiano no Mapeamento de QT
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Modelo Logístico Misto com Classes
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Uma Abordagem Bayesiana para Modelo
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Desempate TécnicoFilipe Jaeger Zab
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10 o Encontro Brasileiro de Estatí
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