论文标题
贝叶斯融合的套索模特
Bayesian Fused Lasso Modeling via Horseshoe Prior
论文作者
论文摘要
贝叶斯融合拉索是稀疏的贝叶斯方法之一，它同时缩小了回归系数及其连续差异。在本文中，我们提出了通过马蹄提前进行贝叶斯融合的套索建模。通过在连续回归系数的差异上假设马蹄铁，提出的方法使我们能够防止这些差异过度障碍。我们还提出了一个贝叶斯六边形操作员，以进行回归，并以马蹄调查的收缩和平等选择（马匹）进行回归。模拟研究和对实际数据的应用表明，所提出的方法比现有方法具有更好的性能。
Bayesian fused lasso is one of the sparse Bayesian methods, which shrinks both regression coefficients and their successive differences simultaneously. In this paper, we propose a Bayesian fused lasso modeling via horseshoe prior. By assuming a horseshoe prior on the difference of successive regression coefficients, the proposed method enables us to prevent over-shrinkage of those differences. We also propose a Bayesian hexagonal operator for regression with shrinkage and equality selection (HORSES) with horseshoe prior, which imposes priors on all combinations of differences of regression coefficients. Simulation studies and an application to real data show that the proposed method gives better performance than existing methods.
