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State Space Representation of Mixture Autoregressive Model
Mohammad Reza Yeganegi , Rahim Chinipardaz
Abstract:   (571 Views)

‎This paper is investigating the mixture autoregressive model with constant mixing weights in state space form and generalization to ARMA mixture model‎. ‎Using a sequential Monte Carlo method‎, ‎the forecasting‎, ‎filtering and smoothing distributions are approximated and parameters f the model is estimated via the EM algorithm‎. ‎The results show the dimension of parameter vector in state space representation reduces‎. ‎The results of the simulation study show that the proposed filtering algorithm has a steady state close to the real values of the state vector‎. ‎Moreover‎, ‎according to simulation results‎, ‎the mean vectors of filtering and smoothing distribution converges to state vector quickly‎.

Keywords: ‎Conditional Dynamic Linear Model‎, ‎EM Algorithm‎, Mixture Autoregressive Model‎, ‎Mixture ARMA Model‎, ‎Mixture Kalman Filter‎, ‎Nonlinear State Space Model.
Full-Text [PDF 6861 kb]   (104 Downloads)    
Type of Study: Applied | Subject: Time Series
Received: 2014/12/29 | Accepted: 2018/08/5
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مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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