Model selection by MCMC computation
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
| Hi-index | 12.05 |
Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan-Quinn criterion (HQC; Hannan, 1980) are used for model specification in autoregressive fractional integrated moving average (ARFIMA) models. Classical model selection criteria require to calculate both model parameters and order. This kind of approach needs much time. However, in the literature, there are proposed methods which calculate model parameters and order at the same time such as reversible jump Markov chain Monte Carlo (RJMCMC) method, Carlin and Chib (CC) method. In this paper, we proposed two new methods that are using RJMCMC method. The proposed methods are compared with classical methods by a simulation study. We obtained that our methods outperform classical methods in most cases.