Matrix analysis and applied linear algebra
Matrix analysis and applied linear algebra
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
An Autoregressive (AR) Model Applied to Eye Tremor Movement, Clinical Application in Schizophrenia
Journal of Medical Systems
Improving subband spectral estimation using modified AR model
Signal Processing
Order selection criteria for vector autoregressive models
Signal Processing
IEEE Transactions on Signal Processing
Hi-index | 0.08 |
The most important problem in data modeling using the AR model is the order selection. Some AR order selection criteria estimate the prediction error and choose the order that minimizes this estimated prediction error. All of these criteria use the same formula for estimating the prediction error from the residual variance for all AR models. However, experimental results show that the relationship between the prediction error and the residual variance depends on the AR model. In this paper, we introduce new formulas for estimating the prediction error using the residual variance. These formulas depend on the AR model, and are obtained through assuming a white Gaussian noise as the input noise to the AR model and assuming that the least-squares-forward (LSF) method is used for estimating the AR coefficients. The performance of the new order selection criteria introduced in this paper is compared with other AR order selection criteria using simulated data. Results show that the new criteria have good performance in estimating the prediction error and in selecting an appropriate order for the AR model.