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Bayesian optimal designs for multi-factor nonlinear models

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Abstract

This article is concerned with the Bayesian optimal design problem for multi-factor nonlinear models. In particular, the Bayesian \(\varPsi _q\)-optimality criterion proposed by Dette et al. (Stat Sinica 17:463–480, 2007) is considered. It is shown that the product-type designs are optimal for the additive multi-factor nonlinear models with or without constant term when the proposed sufficient conditions are satisfied. Some examples of application using the exponential growth models with several variables are presented to illustrate optimal designs based on the Bayesian \(\varPsi _q\)-optimality criterion considered.

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References

  • Atkinson A, Donev A, Tobias R (2007) Optimum experimental designs, with SAS. Oxford University Press, New York

    MATH  Google Scholar 

  • Biedermann S, Dette H, Woods DC (2011) Optimal design for additive partially nonlinear models. Biometrika 98:449–458

    Article  MathSciNet  Google Scholar 

  • Box GEP, Lucas H (1959) Designs of experiments in non-linear situations. Biometrika 46:77–90

    Article  MathSciNet  Google Scholar 

  • Chaloner K, Larntz K (1989) Optimal Bayesian design applied to logistic regression experiments. J Statist Plann Inference 21:191–208

    Article  MathSciNet  Google Scholar 

  • Chaloner K, Verdinelli I (1995) Bayesian experimental design: a review. Statist Sci 10:273–304

    Article  MathSciNet  Google Scholar 

  • Chernoff H (1953) Locally optimal designs for estimating parameters. Ann Math Stat 24:586–602

    Article  MathSciNet  Google Scholar 

  • Dette H (1992) Optimal designs for a class of polynomials of odd or even degree. Ann Stat 20:238–259

    Article  MathSciNet  Google Scholar 

  • Dette H, Haines LM, Imhof LA (2007) Maximin and Bayesian optimal designs for regression models. Stat Sinica 17:463–480

    MathSciNet  MATH  Google Scholar 

  • Dette H, Neugebauer HM (1997) Bayesian \(D\)-optimal designs for exponential regression models. J Stat Plan Inference 60:331–349

    Article  MathSciNet  Google Scholar 

  • Dette H, Wong WK (1996) Optimal Bayesian designs for models with partially specified heteroscedastic structure. Ann Stat 24:2108–2127

    Article  MathSciNet  Google Scholar 

  • Graßhoff U, Großmann H, Holling H, Schwabe R (2007) Design optimality in multi-factor generalized linear models in the presence of an unrestricted quantitative factor. J Stat Plan Inference 137:3882–3893

    Article  MathSciNet  Google Scholar 

  • He L, Yue R-X (2017) \(R\)-optimal designs for multi-factor models with heteroscedastic errors. Metrika 80:717–732

    Article  MathSciNet  Google Scholar 

  • He L (2018) Optimal designs for multi-factor nonlinear models based on the second-order least squares estimator. Stat Probab Lett 137:201–208

    Article  MathSciNet  Google Scholar 

  • Kiefer J (1974) General equivalence theory for optimum designs (approximate theory). Ann Stat 2:849–879

    Article  MathSciNet  Google Scholar 

  • Liu X, Yue R-X, Chatterjee K (2014) A note on \(R\)-optimal designs for multi-factor model. J Stat Plan Inference 146:139–144

    Article  MathSciNet  Google Scholar 

  • Ratkowsky DA (1983) Nonlinear regression modelling. Marcel Dekker, New York

    MATH  Google Scholar 

  • Rodríguez C, Ortiz I (2005) \(D\)-optimum designs in multi-factor models with heteroscedastic errors. J Stat Plan Inference 128:623–631

    Article  MathSciNet  Google Scholar 

  • Rodríguez C, Ortiz I, Martínez I (2015) Locally and maximin optimal designs for multi-factor nonlinear models. Statistics 49:1157–1168

    Article  MathSciNet  Google Scholar 

  • Rodríguez C, Ortiz I, Martínez I (2016) \(A\)-optimal designs for heteroscedastic multifactor regression models. Commun Stat Theory Methods 45:757–771

    Article  MathSciNet  Google Scholar 

  • Schwabe R (1996) Optimum designs for multi-factor models, Lecture Notes in Statistics, vol 113. Springer, New York

  • Wong WK (1994) \(G\)-optimal designs for multifactor experiments with heteroscedastic errors. J Stat Plan Inference 40:127–133

    Article  Google Scholar 

  • Yang M, Zhang B, Huang S (2011) Optimal designs for generalized linear models with multiple design variables. Stat Sinica 21:1415–1430

    Article  MathSciNet  Google Scholar 

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He, L. Bayesian optimal designs for multi-factor nonlinear models. Stat Methods Appl 30, 223–233 (2021). https://doi.org/10.1007/s10260-020-00522-w

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