جستجوی مقالات مرتبط با کلیدواژه
bayesian variable selection
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تکرار جستجوی کلیدواژه bayesian variable selection در مقالات مجلات علمی
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In this paper, we considered a Bayesian hierarchical method using the hyper product inverse momentprior in the ultrahigh-dimensional generalized linear model (UDGLM), that was useful in the Bayesianvariable selection. We showed the posterior probabilities of the true model converge to 1 as the samplesize increases. For computing the posterior probabilities, we implemented the Laplace approximation.The Simplified Shotgun Stochastic Search with Screening (S5) procedure for generalized linear modelwas suggested for exploring the posterior space. Simulation studies and real data analysis using theBayesian ultrahigh-dimensional generalized linear model indicate that the proposed method had better performance than the previous models.Keywords: Ultrahigh dimensional, Nonlocal prior, Optimal properties, Bayesian Variable Selection, Generalized Linear Model&lrm
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The Bayesian variable selection analysis is widely used as a new methodology in air quality control trials and generalized linear models. One of the important and, of course,controversial topics in this area is selection of prior distribution of unknown model parameters. The aim of this study is presenting a substitution for mixture of priors which besidespreservation of benefits and computational efficiencies obviate the available paradoxes andcontradictions. In this research we pay attention to two points of view; empirical and fullyBayesian. Especially, a mixture of priors and its theoretical characteristics is introduced.Finally, the proposed model is illustrated with a real example.Keywords: Bayesian Variable Selection, Mixture of Priors, Bartlett’s Paradox, Information Paradox, Empirical Bayesian analysis
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