Estimação bayesiana no modelo potência normal bimodal assimétrico
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, wh...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Brasil |
| Institución: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/21722 |
| Acceso en línea: | https://repositorio.ufrn.br/jspui/handle/123456789/21722 |
| Access Level: | acceso abierto |
| Palabra clave: | Assimetria Bimodalidade DIC Inferência bayesina MCMC Priori de Jeffreys CNPQ::CIENCIAS EXATAS E DA TERRA::MATEMATICA: MATEMÁTICA APLICADA E ESTATÍSTICA |
| Sumario: | 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. |
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