Nonlinear regression models have widespread applications across diverse scientific disciplines. Achieving precise fitting of the optimal nonlinear model is essential, taking into account the biases inherent in Bayesian optimal design. This study introduces a Bayesian optimal design utilizing the Dirichlet process as a prior. The Dirichlet process is a fundamental tool in exploring Nonparametric Bayesian inference, providing multiple well-suited representations. The research paper presents a novel one-parameter model, termed the ``unit-exponential distribution", specifically designed for the unit interval. Additionally, a representation is employed to approximate the D-optimality criterion, considering the Dirichlet process as a functional tool. Through this approach, the aim is to identify a nonparametric Bayesian optimal design.
Abdollahi Nanvapisheh, A., Jafari, H., & Khazaei, S. (2023). Nonparametric Bayesian optimal designs for unit exponential nonlinear model. Journal of Statistical Modelling: Theory and Applications, 4(1), 59-73. doi: 10.22034/jsmta.2024.20475.1114
MLA
Anita Abdollahi Nanvapisheh; Habib Jafari; Soliman Khazaei. "Nonparametric Bayesian optimal designs for unit exponential nonlinear model". Journal of Statistical Modelling: Theory and Applications, 4, 1, 2023, 59-73. doi: 10.22034/jsmta.2024.20475.1114
HARVARD
Abdollahi Nanvapisheh, A., Jafari, H., Khazaei, S. (2023). 'Nonparametric Bayesian optimal designs for unit exponential nonlinear model', Journal of Statistical Modelling: Theory and Applications, 4(1), pp. 59-73. doi: 10.22034/jsmta.2024.20475.1114
VANCOUVER
Abdollahi Nanvapisheh, A., Jafari, H., Khazaei, S. Nonparametric Bayesian optimal designs for unit exponential nonlinear model. Journal of Statistical Modelling: Theory and Applications, 2023; 4(1): 59-73. doi: 10.22034/jsmta.2024.20475.1114
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