GARMA models, a new perspective using Bayesian methods and transformations

• Ano de defesa: 2016
• Autor(a) principal: Andrade, Breno Silveira de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Abordagem Bayesiana; ARMA generalizado; ARMA transformado generalizado; Bayesian approach; Continuous distributions; Discrete distributions; Distribuições contínuas; Distribuições discretas; Generalized ARMA model; Transformed generalized ARMA model
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/104/104131/tde-27032017-161141/

Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work presents the GARMA model with discrete distributions and application of resampling techniques to this class of models. We also proposed The Bayesian approach on GARMA models. The TGARMA (Transformed Generalized Autoregressive Moving Average) models was proposed, using the Box-Cox power transformation. Last but not least we proposed the Bayesian approach for the TGARMA (Transformed Generalized Autoregressive Moving Average).