Hidden Markov Mixture Autoregressive Models: Parameter Estimation
Statistics Theory
2011-05-17 v1 Statistics Theory
Abstract
This report introduces a parsimonious structure for mixture of autoregressive models, where the weighting coefficients are determined through latent random variables as functions of all past observations. These variables follow a hidden Markov model. We modify EM and Baum-Welch algorithms to estimate the parameters of the model.
Cite
@article{arxiv.1105.2891,
title = {Hidden Markov Mixture Autoregressive Models: Parameter Estimation},
author = {S. H. Alizadeh and S. Rezakhah},
journal= {arXiv preprint arXiv:1105.2891},
year = {2011}
}
Comments
10 pages