Bayesian Variable Selection in Regression Models using The Laplace Approximation.
محل انتشار: نشریه علم داده و مدل سازی، دوره: 1، شماره: 1
سال انتشار: 1399
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 317
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شناسه ملی سند علمی:
JR_JCSM-1-1_012
تاریخ نمایه سازی: 18 فروردین 1400
چکیده مقاله:
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 besides preservation of benefits and computational efficiencies obviate the available paradoxes and contradictions. In this research we pay attention to two points of view; empirical and fully Bayesian. Especially, a mixture of priors and its theoretical characteristics is introduced. Finally, the proposed model is illustrated with a real example.
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نویسندگان
sima naghizadeh
national organization for educational testing