Minimum Message Length Inference and Parameter Estimation of Autoregressive and Moving Average Models
نویسنده
چکیده
This technical report presents a formulation of the parameter estimation and model selection problem for Autoregressive (AR) and Moving Average (MA) models in the Minimum Message Length (MML) framework. In particular, it examines suitable priors for both classes of models, and subsequently derives message length expressions based on the MML87 approximation. Empirical results demonstrate the new MML estimators outperform several benchmark parameter estimation and model selection criteria on various prediction metrics. Daniel Schmidt is with Monash University Clayton School of Information Technology Clayton Campus Victoria 3800, Australia Telephone: +61 3 9905 3414, Fax: +61 3 9905 5146 Email: [email protected]
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