“School of Biological Sciences”

Back to Papers Home
Back to Papers of School of Biological Sciences

Paper   IPM / Biological Sciences / 17400
School of Biological Sciences
  Title:   Bayesian Shrinkage Estimators of Quality Parameters in Ultrahigh-Dimensional Generalized Linear Models.
1.  Farzad Eskandari
2.  Robabeh Hosseinpour Samim Mamaghani
3.  Vahid Rezaei Tabar
  Status:   Published
  Journal: Journal of Quality Engineering and Management
  Year:  2023
  Supported by:  IPM
One of the basic issues in Ultrahigh-dimensional data analysis is fitting the optimal model and estimating its unknown quality parameters in such a way that it can correctly interpret the structure of the investigated data. In this article, we compare two non-local hyper priors: hyper product moment and hyper product inverse moment priors in determining the optimal model at the same time as estimating the parameters in variable selection using Bayesian Shrinkage in ultrahigh-dimensional generalized linear models. In order to compute the posterior probabilities, the Laplace approximation method was used, and to select the optimal model in the model space of posterior probabilities, Simplified shotgun stochastic search algorithm with screening (S5) for GLMs was used along with screening. Finally, through the study of simulation and real data analysis, the effectiveness of the above Bayesian Shrinkage methods has been evaluated with the ISIS-LASSO and ISIS-SCAD method. The advantage of the model is shown.

Download TeX format
back to top
scroll left or right