Continuous-time AR process parameter estimation from discrete-time data

The problem of estimating continuous-time autoregressive process parameters from discrete-time data is considered. The basic approach used here is based on replacing the derivatives in the model by discrete-time di erences, forming a linear regression and using the least squares method. It is known, however, that all standard approximations of the highest order derivative give a biased least squares estimate even as the sampling interval tends to zero. Some of our previous approaches to overcome this problem are brie y reviewed. Then two new methods are presented. One of them, termed bias compensation, can be easily implemented e ciently in an order recursive manner. Comparative simulation results are also presented.