“School of Biological Sciences”
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| Paper IPM / Biological Sciences / 17400 |
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| Abstract: | |||||||
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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.
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