Criterion-robust optimal designs for model discrimination and parameter estimation: Multivariate polynomial regression case

Consider the problem of discriminating between two polynomial regres- sion models on the q-cube (−1, 1) q , q ≥ 2, and estimating parameters in the models. To find designs which are efficient for both model discrimination and parameter es- timation, Zen and Tsai (2002) proposed a multiple-objective optimality criterion for the univariate case. In this work, taking the same Mγ -criterion which uses weight γ (0 ≤ γ ≤ 1) for model discrimination and 1 − γ for parameter estimation, the corresponding Mγ -optimal product design is investigated. Based on the maximin principle on the Mγ -efficiency of any Mγ� -optimal product design, a criterion-robust optimal product design is proposed.

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