کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7546025 1489620 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian variable selection with strong heredity constraints
ترجمه فارسی عنوان
انتخاب متغیر باینز با محدودیت های ارثی قوی
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آمار و احتمال
چکیده انگلیسی
In this paper, we propose a Bayesian variable selection method for linear regression models with high-order interactions. Our method automatically enforces the heredity constraint, that is, a higher order interaction term can exist in the model only if both of its parent terms are in the model. Based on the stochastic search variable selection George and McCulloch (1993), we propose a novel hierarchical prior that fully considers the heredity constraint and controls the degree of sparsity simultaneously. We develop a Markov chain Monte Carlo (MCMC) algorithm to explore the model space efficiently while accounting for the heredity constraint by modifying the shotgun stochastic search algorithm Hans et al. (2007). The performance of the new model is demonstrated through comparisons with other methods. Numerical studies on both real data analysis and simulations show that our new method tends to find relevant variable more effectively when higher order interaction terms are considered.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the Korean Statistical Society - Volume 47, Issue 3, September 2018, Pages 314-329
نویسندگان
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