Isobel Clark wrote:
I would think what they mean is that each order of polynomial has to be
balanced between the 'drift' at the actual estimated point and the
weighted average of the samples which proovides the estimator. For this
you have to introduce an extra lamda and an extra equation on the
kriging system which guarantees the unbiassedness of the estimate.
Sorry but I don't understand what you mean by this.
I've been doing some more thinking and reading and here's my GUESS --
please correct me if I'm wrong:
Suppose a RF Z(x) can be modeled as:
Z(x) = m(x) + Y(x)
where m(x) is the drift which is modeled as "weighted" sum of
polynomials of order up to k (e.g. if k = 2, drift is w[0] + w[1].x +
w[2].y + w[3].xy + w[4].x² + w[5].y²) and Y(x) a fluctuation or residual
about this drift. Removing this drift would require somehow finding
values for the weights such that the weighted sum *somehow* becomes zero
thus annihilating the *effect* of the polynomials.
???
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