Estimation of continuous-time autoregressive model from finely sampled data
Dinh-Tuan Pham · IEEE Transactions on Signal Processing · 2000
We extend our two earlier continuous-time estimation methods for continuous-time autoregressive (CAR) model to derive estimators using only finely sampled discrete-time data. The approach is based on the approximation of derivatives by divided differences, coupled with some bias correction. Two types of estimators are provided, having bias of the order O(h) or of O(h/sup 2/) respectively, for small sampling interval h. The procedures are computationally efficient and always yield a stable autoregressive polynomial. Simulations show that their bias are quite low.