Abordagem estatística em modelos para séries temporais de contagem

• Ano de defesa: 2013
• Autor(a) principal: Andrade, Breno Silveira de
• Orientador(a): Andrade Filho, Marinho Gomes de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Estatística - PPGEs
• País: BR
• Palavras-chave em Português: Análise de séries temporais; Inferência bayesiana; Modelos estatísticos; Modelos GARMA; Modelo INGARCH; Modelo GLARMA. Distribuição de Poisson; Distribuição binomial negativa; Inferência clássica
• Palavras-chave em Inglês: INGARCH model; GLARMA model; GARMA model; Poisson distribution; Negative binomial distribution; Classic inference; Bayesian inference
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::ESTATISTICA
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/4571

In this work, it was estudied the models INGARCH , GLARMA and GARMA to model count time series data with Poisson and Negative Binomial discrete conditional distributions. The main goal was analyze in classic and bayesian approach, the adequability and goodness of fit of these models, also the contruction of credibility intervals about each parameter. To the Bayesian study, was cosiderated a joint prior distribuition that satisfied the conditions of each model and got a posterior distribution. This aproach presents too some criterion selection like (EBIC), (DIC) and ordenaded predictive conditional density (CPO) for Bayesian cases and (BIC) for classic cases. A simulation study was done to check the maximum likelihood estimator consistency in classic approach and has used criterion selection classic and Bayesian to choose the order of each model. An Analysis has made in a real data set realized as final stage as, these data consist the number of financial transactions in 30 minutes. These results have made in a classical and Bayesian approach , and discribed the data caracteristic.