Nonparametric Bayesian optimal designs for exponential regression model
| Autor: | Manizheh Goudarzi, Habib Jafari, Soleiman Khazaei |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Optimal design Mathematical optimization 021103 operations research Bayesian probability 0211 other engineering and technologies 02 engineering and technology Space (mathematics) 01 natural sciences Dirichlet process 010104 statistics & probability ComputingMethodologies_PATTERNRECOGNITION Distribution function Modeling and Simulation Prior probability Statistics Nonparametric bayesian 0101 mathematics Mathematics Parametric statistics |
| Zdroj: | Communications in Statistics - Simulation and Computation. :1-11 |
| ISSN: | 1532-4141 0361-0918 |
| DOI: | 10.1080/03610918.2019.1593454 |
| Popis: | Constructing the Bayesian optimal design depends on the choice of a prior distribution for the unknown parameter. Lacking informative or historical knowledge of the parameter, a parametric Bayesian approach cannot be expected in complex statistical problems. In this regard, a nonparametric Bayesian approach can be used, in which random prior distribution is considered. The Dirichlet process is employed as a prior on the space of distribution functions. In this paper, a non-parametric Bayesian approach is incorporated into an optimal design criterion. This method is illustrated by an example. |
| Databáze: | OpenAIRE |
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