Estimação bayesiana no modelo potência normal bimodal assimétrico

• Ano de defesa: 2016
• Autor(a) principal: Souza, Isaac Jales Costa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Brasil; UFRN; PROGRAMA DE PÓS-GRADUAÇÃO EM MATEMÁTICA APLICADA E ESTATÍSTICA
• Palavras-chave em Português: Assimetria; Bimodalidade; DIC; Inferência bayesina; MCMC; Priori de Jeffreys; CNPQ::CIENCIAS EXATAS E DA TERRA::MATEMATICA: MATEMÁTICA APLICADA E ESTATÍSTICA
• Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/21722

In this paper it is presented a Bayesian approach to the bimodal power-normal (BPN) models and the bimodal asymmetric power-normal (BAPN). First, we present the BPN model, specifying its non-informative and informative parameter α (bimodality). We obtain the posterior distribution by MCMC method, whose feasibility of use we tested from a convergence diagnose. After that, We use different informative priors for α and we do a sensitivity analysis in order to evaluate the effect of hyperparameters variation on the posterior distribution. Also, it is performed a simulation to evaluate the performance of the Bayesian estimator using informative priors. We noted that the Bayesian method shows more satisfactory results when compared to the maximum likelihood method. It is performed an application with bimodal data. Finally, we introduce the linear regression model with BPN error. As for the BAPN model we also specify informative and uninformative priors for bimodality and asymmetry parameters. We do the MCMC Convergence Diagnostics, which is also used to obtain the posterior distribution. We do a sensitivity analysis, applying actual data in the model and we introducing the linear regression model with PNBA error.