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Abstract
In this project we study problems of inference and forecasting in autoregressive models with periodic correlation from a Bayesian perspective. Normality and unimodality assumptions are rarely verified in practice and the usual approach is to try Box-Cox transformations to obtain approximate normality and stabilize the periodic variance. More recently, mixture models were developed to take into account asymmetry and multimodality.
Participants
- Ricardo Ehlers (UFPR)
- Marinho Gomes de Andrade (USP)
Some references
<bibtex> @Article{lewis,
author = {Lewis, P.A.W. and Ray, B.K.}, title = {Nonlinear Modelling of Periodic Threshold Autoregressions using TSMARS}, journal = {Journal of Time Series Analysis}, year = {2002}, volume = {23}, number = {4}, pages = {459-471}
} @Article{shao06,
author = {Shao, Q}, title = {Mixture Periodic Autoregressive Time Series Models}, journal = {Statistics and Probability Letters}, year = {2006}, volume = {76}, pages = {609-618},
} </bibtex>