Nonparametric Bayesian optimal designs for unit exponential nonlinear model

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Abdollahi Nanvapisheh Anita; Jafari Habib; Khazaei Soleiman

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Journal Paper

Paper Information

Journal: JOURNAL OF STATISTICAL MODELLING: THEORY AND APPLICATIONS Year:2023 | Volume:4 | Issue:1 Page(s): 59-73

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Information Journal Paper

Title

Nonparametric Bayesian optimal designs for unit exponential nonlinear model

Author(s)

Abdollahi Nanvapisheh Anita | Jafari Habib | Khazaei Soleiman | Issue Writer Certificate

Keywords

Bayesian optimal design

D-optimal design

Dirichlet process

Nonparametric Bayesian

Stick-breaking prior

Unit exponential model

Abstract

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.

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