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Bayesian Nonlinear Regression Models with Scale Mixtures ofSkew Normal Distributions: Estimation and Case InfluenceDiagnosticsVicente Garibay Cancho ∗Departamento de Matemática Aplicada e Estatística -USPVictor Hugo LachosDepartamento de Estatística-UnicampMarinho G. AndradeDepartamento de Matemática Aplicada e Estatística -USPThe purpose of this paper is to develop a Bayesian analysis for nonlinear regression models underscale mixtures of skew-normal distributions (Branco and Dey, 2001). This novel class of models providesa useful generalization of the symmetrical nonlinear regression models (Galea et al., 2005) since the errordistributions cover both skewness and heavy–tailed distributions such as the skew-t, skew-slash and theskew-contaminated normal distributions. The main advantage of these class of distributions is that theyhave a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo(MCMC) methods to simulate samples from the joint posterior distribution. In order to examine therobust aspects of this flexible class, against outlying and influential observations, we present a Bayesiancase deletion influence diagnostics based on the Kullback–Leibler divergence as proposed by Cho et al.(2009). Further, some discussions on models selection criteria are given. The developed proceduresare illustrated considering a simulated data, and a real data previously analyzed under normal andskew-normal nonlinear regression models.∗ Apresentador/Speaker29
Partitioning Diversity Measure for Hierarchical Beta andDirichlet ModelsDipak DeyDepartment of Statistics, University of Connecticut, USAKent HolsingerDepartment of Ecology and Evolutionary Biology, University ofConnecticut, USAJuan C. Vivar ∗Department of Statistical Sciences, Duke University, USABayesian approaches are widely used to capture over dispersion in binomial and multinomial datathrough beta binomial and Dirichlet multinomial model. Consequently, the same approaches also producea partition on diversity measure within and among levels in multilevel binomial and multinomialmodels. In this paper, we consider situations in which the prior distribution of a parameter vector in thedistribution of the observable binomial and multinomial data contains a hyper parameter vector, whichitself has a hyper distribution and so forth. We first develop a novel multilevel product partition resultwhich shows how the total diversity is partitioned between various hierarchical levels. Then we establishthat the gain in information decreases as one moves to higher levels of hierarchy. We also establish theconnection with the clumped multinomial data. Finally, we apply our methodology to simple multi locustwo allele genetic models to show how the diversity measure is factored into diversity between populationsand within loci. Extensions of the model to incorporate hierarchical levels with multiple alleles perlocus are also considered.Keywords: Beta binomial, Dirichlet multinomial, diversity measure.∗ Apresentador/Speaker30
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 and 22: Bayesian Non-Parametric Inference f
Page 23 and 24: Bayesian Computing with INLAHåvard
Page 27 and 28: Bayesian Inference for a Skew-Norma
Page 29: Bayesian Coalescent Modeling of the
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
Page 61 and 62: Test for Symmetry in Contingency Ta
Page 63 and 64: Aplicações de Inferência Bayesia
Page 65 and 66: Uma Abordagem Bayesiana do Modelo M
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
Page 73 and 74: Comparação de um Modelo Dinâmico
Page 75 and 76: Estimando Variações de Temperatur
Page 77 and 78: A Bayesian Approach to Multivariate
Page 79 and 80: 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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10 o Encontro Brasileiro de Estatí
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