کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7547417 | 1489750 | 2016 | 23 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Consistency of Bayes factors under hyper g-priors with growing model size
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
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چکیده انگلیسی
In this paper, we examine Bayes factor consistency in the context of Bayesian variable selection for normal linear regression models. We take a hierarchical Bayesian approach using a hyper-g prior (Liang et al. (2008) JASA). There are two regimes for computing Bayes factors, which differ in the choice of the base model. For both these regimes, we study conditions under which Bayes factors are consistent when the number of all the potential regressors grows with sample size n. This situation is not fully understood in the current literature, but has gained increasing importance recently. In the present case, Bayes factors are not analytically tractable and are calculated via Laplace approximation. A rigorous justification for these high-dimensional Laplace approximations is also provided.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Statistical Planning and Inference - Volume 173, June 2016, Pages 64-86
Journal: Journal of Statistical Planning and Inference - Volume 173, June 2016, Pages 64-86
نویسندگان
Ruoxuan Xiang, Malay Ghosh, Kshitij Khare,