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- W2394925164 abstract "This paper considers linear models with misspecification of the form f(x )= E(y|x )= p=1 θj gj(x )+ h(x), where h(x) is an unknown function. We assume that the true response function f comes from a reproducing kernel Hilbert space and the estimates of the parameters θj are obtained by the standard least squares method. A sharp upper bound for the mean squared error is found in terms of the norm of h. This upper bound is used to choose a design that is robust against the model bias. It is shown that the continuous uniform design on the experimental region is the all-bias design. The numerical results of several examples show that all-bias designs perform well when some model bias is present in low dimensional cases." @default.
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- W2394925164 title "ROBUST DESIGNS FOR FITTING LINEAR MODELS WITH MISSPECIFICATION" @default.
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