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Destructive Weighted Poisson Cure Rate ModelsJosemar RodriguesDepartamento de Estatística, Universidade Federal de São CarlosIn this paper, we develop a flexible cure rate survival model by assuming the number of competingcauses of the event of interest to follow a compound weighted Poisson distribution. This model is moreflexible in terms of dispersion than the promotion time cure model. Moreover, it gives an interestingand realistic interpretation of the biological mechanism of the occurrence of the event of interest as itincludes a destructive process of the initial risk factors in a competitive scenario. In other words, whatis recorded is only the undamaged portion of the original number of risk factors. An example with a realdataset is worked out from the Bayesian point of view to illustrate different models comprised by ourformulation. Joint work with Vicente A. Garibay and Mario de Castro.Keywords: competing risks, cure rate models, long-term survival models, weighted Poisson distribution,Conway–Maxwell Poisson (COM–Poisson) distribution.21
Bayesian Computing with INLAHåvard RueDepartment of Mathematical Sciences,Norwegian University of Science and Technology, NorwayMany models in statistics can now to analysed using quick-to-compute integrated nested Laplaceapproximations (INLA) instead tedious MCMC sampling. In this talk I will present the main ideas ofthis approach, which models it can deal with and demonstrate how the analysis can be done in practicefrom within R. The software is available from www.r-inla.org.Bridging the gap between Gaussian fields and Gaussian Markov random fields using stochastic partialdifferential equationsGaussian fields (GFs) and Gaussian Markov random fields (GMRFs) specify both multivariate Gaussiandistributions, but are still very different in the way the distribution is specified. GMRFs are naturallyspecified using full conditionals (with the consequence that marginal properties are transparentin the parametrisation) and has very good computational properties, whereas GFs are specified usingcovariance-functions but has less appealing computational properties. In this talk, I will discuss how tobridge GFs and GMRFs, using stochastic partial differential equations which allow us to go seamlesslybetween the GF and GMRF representation and exploit the best properties of both GFs and GMRFs.The consequence of these results is wide-ranging.22
Page 4 and 5: 10 o Encontro Brasileiro de Estatí
Page 9 and 10: Particle LearningCarlos M. Carvalho
Page 12 and 13: Uso de Métodos Bayesianos em Anál
Page 15 and 16: Bayesian Inference for Aggregated F
Page 17 and 18: Product Partition Models with Corre
Page 19 and 20: “Objective Bayes” - A Dangerous
Page 21: Bayesian Non-Parametric Inference f
Page 27 and 28: Bayesian Inference for a Skew-Norma
Page 29 and 30: Bayesian Coalescent Modeling of the
Page 31 and 32: Partitioning Diversity Measure for
Page 33 and 34: Exact Simulation and Bayesian Infer
Page 35 and 36: Spatially Varying Autoregressive Pr
Page 37 and 38: Bayesian Beta Dynamic Model and App
Page 51 and 52: Generalized Exponential Distributio
Page 53 and 54: Análise Bayesiana da Priori Jeffer
Page 55 and 56: Neighborhood Dependence in Bayesian
Page 57 and 58: Aplicação do Algoritmo MCMC de Sa
Page 59 and 60: Decisões de Investimento e Restri
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Page 63 and 64: Aplicações de Inferência Bayesia
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Page 67 and 68: Accounting for Latent Spatio-Tempor
Page 69 and 70: Uma Abordagem Bayesiana para Proces
Page 71 and 72: A Bayesian Analysis for the General
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Comparação de um Modelo Dinâmico
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Estimando Variações de Temperatur
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A Bayesian Approach to Multivariate
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Bayesian Inference for Gamma Modula
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Computational Tools for Comparing A
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Modelo Partição Produto com “Cl
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BIC Criterion for Minimal Markov Mo
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Estimadores Lineares Bayesianos em
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Modelos de Volatilidade Estocástic
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Processo de Difusão Multivariado c
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Generalized Latent Factor Models fo
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Measuring Vulnerability via Spatial
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Medidas do Valor Preditivo de um Mo
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Aproximações Analíticas para Pos
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Modelo de Risco Logístico Dependen
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A Practical Approach to Elicit Mult
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Modelos Dinâmicos para Deformaçã
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A Semiparametric Bayesian Approach
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Modelo Weibull Modificado de Longa
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Abordagem Bayesiana para Modelos GA
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Modelo Bayesiano Geral de Classe La
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Métodos de Estimação em Modelos
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LASSO Bayesiano no Mapeamento de QT
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Estudo das Matrizes de Covariância
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Sensibilidade da Inferência Bayesi
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Análise Bayesiana para o Estudo de
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Uma Abordagem Bayesiana para Modelo
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Construção de Tábuas de Mortalid
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Um Procedimento Bayesiano para a An
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Um Novo Modelo para Dependência Es
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Distribuição Geométrica Exponenc
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Morbidade Pulmonar e Condições Cl
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Standard Setting for a Rasch Poisso
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Modelo Logístico Misto com Classes
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Metodologia Bayesiana para Detecç
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Modelos Espaço-Temporais para Dado
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Comparação de Modelos para a Iden
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Modelos Multivariados de Regressão
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Estimação Bayesiana em Modelos de
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Previsão de Resultados de Jogos de
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Uma Abordagem Bayesiana para Modelo
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Análise Bayesiana do Modelo Log-Bi
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Desempate TécnicoFilipe Jaeger Zab
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