On the linear least-square prediction problem
نویسندگان
چکیده
An efficient algorithm is derived for the recursive computation of the filtering and all types of linear leastsquare prediction estimates (fixed-point, fixed-interval, and fixed-lead predictors) of a nonstationary signal vector. It is assumed that the signal is observed in the presence of an additive white noise which can be correlated with the signal. The methodology employed only requires that the covariance functions involved are factorizable kernels and then it is applicable without the assumption that the signal verifies a state-space model.
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