Optimal estimation of a random signal from partially missed data

We provide a new technique for random signal estimation under the constraints that the data is corrupted by random noise and moreover, some data may be missed. We utilize nonlinear filters defined by multi-linear operators of degree r, the choice of which allows a trade-off between the accuracy of the optimal filter and the complexity of the corresponding calculations. A rigorous error analysis is presented.