Optimal Estimation of the Parameters of Normal Regression for Extended Observation Models

In the framework of classical normal regression theory, we solve the problem of optimal treatment of discrete measurements for the case where the observation vector includes not only the sample values of a process, but also the sample values of its derivatives of various orders. We consider the traditional problems of reconstructing the parameters of normal regression in the polynomial class and in the class of functions linear in parameters.