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Scale Mixtures of Normal Distributions: A Bayesian Analysisof Stochastic Volatility in MeanCarlos Antonio Abanto-Valle ∗DME-<strong>UFRJ</strong>Helio dos Santos MigonDME-<strong>UFRJ</strong>Victor Hugo LachosIMECC-UNICAMPThe stochastic volatility in mean (SVM) model using the class of symmetric scale mixtures of normal(SMN) distributions is introduced in this article. The SMN family distributions is an attractive classof symmetric distributions that includes the normal, Student-t, slash and contaminated normal distributionsas special cases, providing a robust alternative to estimation in SVM models in the absenceof normality. Using a Bayesian paradigm, an efficient method based on Markov chain Monte Carlo(MCMC) is developed for parameter estimation. Additionally, we develop a second-order approximationmethod to the usual Auxiliary Particle Filter (APF) in order to estimate efficiently the log-likelihoodfunction to model comparison, such as the Bayesian Predictive Information Criteria (BPIC). The methodsdeveloped are applied to analyze daily stock returns data on São Paulo Stock, Mercantile & FuturesExchange index (IBOVESPA). Bayesian model selection criteria as well as out-of- sample forecastingresults reveal that the SVM model with slash distribution provides significant improvement in model fitas well as prediction to the IBOVESPA data over the usual normal model.Keywords: Markov chain Monte Carlo, non-Gaussian and nonlinear state space models, scale mixtureof normal distributions, stochastic volatility in mean.∗ Apresentador/Speaker25
Bayesian Inference for a Skew-Normal IRT Model under theCentred ParameterizationCaio Lucidius Naberezny Azevedo ∗Department of Statistics, University of Campinas, BrazilHeleno BolfarineDepartment of Statistics, University of São Paulo, BrazilDalton Francisco de AndradeDepartment of Informatics and Statistics, Federal University of SantaCatarina, BrazilItem response theory (IRT) comprises a set of statistical models which are usefull in many fieldswhere there is an interest in studying latent variables. These latent variables (or characteristcs) aredirectly considered in the Item Response Models (IRM) and they are usually called latent traits. Anusual assumption, for parameter estimation of the IRM, considering one group of examinees, is to assumethat the latent traits are random variables that follow a standard normal distribution. However, manyworks suggest that this assumption does not apply in many cases. Furthermore, when this is the case,the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, thischaracteristic must be taken into account. In this paper we present an alternative for latent traitmodelling based on the so-called skew-normal distribution. We used the centred parameterization. Thisapproach ensures the model identifiability. Also, a Metropolis-Hastings within Gibbs sampling basedalgorithm was built for parameter estimation through an augmented data approach. A simulation studywas performed in order to assess the recovering of all parameters of the proposed estimation algorithmsand the effects of asymmetry of the latent traits distribution in such estimation. Also, a comparisonof our approach with other widely used estimation methods was considered. The results indicated thatour proposed algorithm recovers properly all parameters. Stronger the asymmetry level, better is theperformance of our approach compared to the others, specially for small sample sizes. Furthermore, weanalyzed a real data set which presents traits of asymmetry. The results obtained using our approachconfirmed the presence of strong negative asymmetry.∗ Apresentador/Speaker26
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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
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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
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
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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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10 o Encontro Brasileiro de Estatí
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