Criterion-robust Optimal Designs for Model Discrimination and Parameter Estimation: Multivariate Polynomial Regression Case
نویسندگان
چکیده
Consider the problem of discriminating between two polynomial regression models on the q-cube [−1, 1] , q ≥ 2, and estimating parameters in the models. To find designs which are efficient for both model discrimination and parameter estimation, 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 theMγ-efficiency of anyMγ′ -optimal product design, a criterion-robust optimal product design is proposed.
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