Bayesian Information Based Criteria for Poisson Regression Models

Authors

  • Hillary Ali Department of Mathematics, University of Jos, Nigeria.
  • Nwaosu S. C. Department of Statistics, Joseph Sawuaan Tarka University of Agriculture, Makurdi, Nigeria.
  • Lasisi K. E. Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P. M. B. 0248, Bauchi, Nigeria
  • A. Abdulkadir Department of Mathematical Sciences, Abubakar Tafawa Balewa University, P. M. B. 0248, Bauchi, Nigeria.

Keywords:

Bayesian Poisson Regression, D-Optimality, A-Optimality, Efficiency

Abstract

Bayesian optimal designs for count responses analyzed with Poisson regression describing a linear health effect are considered. To overcome the problem of dependence of Bayesian designs on the choice of prior distributions, Bayesian D & A-optimal designs are proposed for Poisson regression models. The results showed that the optimal number of time points depends on the subject-to-measurement. Also, Bayesian D & A-optimal designs are highly efficient and robust under models considered when implementing the efficiencies of designs with the Bayesian D- & A-optimal designs in modelling students’ academic performance. The best design is found in one out of ten tries.

References

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Published

2022-02-25

How to Cite

Ali, H. ., Nwaosu S. C., Lasisi K. E., & A. Abdulkadir. (2022). Bayesian Information Based Criteria for Poisson Regression Models. International Journal of Formal Sciences: Current and Future Research Trends, 13(1), 22–40. Retrieved from https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/620

Issue

Vol. 13 No. 1 (2022)

Section

Articles

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