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Robust prediction and extrapolation designs for nonlinear regression with imprecision

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Abstract

We consider the general situation of fitting an assumed nonlinear regression model which is possibly misspecified. The minimax designs for both response prediction and extrapolation in biased nonlinear regression models are discussed. We extend previous work of others from linear response or a given function of linear response to intrinsically nonlinear response. Several examples are illustrated such as designing for a yield-fertilizer model, a simple compartmental model, and a Michaelis–Menten model.

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Acknowledgments

This research is supported by the Natural Sciences and Engineering Research Council of Canada. The authors are thankful to two anonymous referees for their helpful comments.

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Authors and Affiliations

  1. Department of Mathematics, Brock University, 500 Glenridge Ave., St. Catharines, ON, L2S 3A1, Canada

    Xiaojian Xu

  2. Department of Mathematics, Brock University, St. Catharines, Canada

    Arnold Chen

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  1. Xiaojian Xu
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  2. Arnold Chen
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Correspondence to Xiaojian Xu.

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Xu, X., Chen, A. Robust prediction and extrapolation designs for nonlinear regression with imprecision. METRON 72, 25–44 (2014). https://doi.org/10.1007/s40300-013-0021-0

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  • DOI: https://doi.org/10.1007/s40300-013-0021-0

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