Performance of discrete-time predictors of continuous-time stationary processes

The asymptotic performance of linear predictors of continuous-time stationary processes is studied from observations at n sampling instants on a fixed observation interval. Both optimal and simpler choices of predictor coefficients are considered, using uniform sampling as well as nonuniform sampling tailored to the statistics of the process under prediction. The focus is on stationary processes with rational spectral densities and the asymptotic performance for cases with and without a quadratic-mean derivatives is obtained. The analytical results are supplemented by numerical examples depicting small- and large-sample-size performance