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<p>Nonlinear regression models find extensive application across numerous scientific fields. Accurately fitting an optimal nonlinear model is essential, particularly with consideration for biases in Bayesian optimal design. This study presents optimal designs for Gumbel regression model, utilizing both Bayesian and nonparametric Bayesian methods. A nonparametric Bayesian approach was applied by introducing a Dirichlet Process (DP) prior over the space of distribution functions.</p>

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Locally, Bayesian and Nonparametric Bayesian Optimal Designs for Gumbel Regression Model

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Anita Abdollahi Nanvapisheh,
  • Soleiman Khazaei,
  • Habib Jafari,
  • Reza A. Nazari

摘要

Nonlinear regression models find extensive application across numerous scientific fields. Accurately fitting an optimal nonlinear model is essential, particularly with consideration for biases in Bayesian optimal design. This study presents optimal designs for Gumbel regression model, utilizing both Bayesian and nonparametric Bayesian methods. A nonparametric Bayesian approach was applied by introducing a Dirichlet Process (DP) prior over the space of distribution functions.