Referring to the earlier treatment of linear least-squares regression, we saw that the key step in obtaining the normal equations was to take the partial derivatives of the objective function with respect to each parameter, setting these equal to zero. The general form of this operation is [Pg.49]

Firstly, various criteria for estimation, different from the least square E [P(x)-P (x)], may now be considered. Consider a general loss function L(e), function of the error of estimation e p(x) -p (x). The objective Is to build an estimator that would minimize the expected value of that loss function, and more precisely. Its conditional expectation given the N data values and configuration. [Pg.113]

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