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Robust designs for polynomial regression by maximizing a minimum of \(D\)- and \(D_1\)-efficiencies. (English) Zbl 1012.62080
Summary: In the common polynomial regression of degree m we determine the design which maximizes the minimum of the \(D\)-efficiency in the model of degree m and the \(D_1\)-efficiencies in the models of degree \(m-j,\dots, m +k\) \((j, k\geq 0\) given). The resulting designs allow an efficient estimation of the parameters in the chosen regression and have reasonable efficiencies for checking the goodness-of-fit of the assumed model of degree \(m\) by testing the highest coefficients in the polynomials of degree \(m-j,\dots, m +k\).
Our approach is based on a combination of the theory of canonical moments and general equivalence theory for minimax optimality criteria. The optimal designs can be explicitly characterized by evaluating certain associated orthogonal polynomials.

62K05 Optimal statistical designs
33C45 Orthogonal polynomials and functions of hypergeometric type (Jacobi, Laguerre, Hermite, Askey scheme, etc.)
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