New penalized Bayesian adaptive lasso binary regression
DOI:
https://doi.org/10.21533/pen.v9.i1.731Abstract
The scale mixture of normal mixing with Rayleigh as representation of Laplace prior of has introduced by Flaih et al. [1]. We employed this new scale mixture for the adaptive lasso Binary regression. New hierarchical model is considering, as well the Gibbs sampler algorithm in introduced. We considering the new penalized Bayesian adaptive lasso in Binary regression as variable selection method in case of presenting they high dimensional data. The new proposed model can overcome the multicollinearity problem in predictor variables. We conducting simulation analysis, as well as real data application to show the performance of the proposed method.
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