论文标题
基于经验可能性的贝叶斯弹性网
Bayesian Elastic Net based on Empirical Likelihood
论文作者
论文摘要
我们提出了一个使用经验可能性的贝叶斯弹性网，并开发了对汉密尔顿蒙特卡洛进行后抽样的有效调整。提出的模型放松了对误差分布身份的假设，当变量高度相关时性能很好，并通过提供回归系数的后验分布来实现更直接的推理。在贝叶斯经验可能性中实施的哈密顿蒙特卡洛方法克服了后验分布缺乏封闭的分析形式，其域是非convex的挑战。我们开发了贝叶斯经验可能性的LeapFrog参数调整算法。我们还表明，回归系数的后验分布在渐近上正常。模拟研究和实际数据分析证明了所提出方法在预测准确性方面的优势。
We propose a Bayesian elastic net that uses empirical likelihood and develop an efficient tuning of Hamiltonian Monte Carlo for posterior sampling. The proposed model relaxes the assumptions on the identity of the error distribution, performs well when the variables are highly correlated, and enables more straightforward inference by providing posterior distributions of the regression coefficients. The Hamiltonian Monte Carlo method implemented in Bayesian empirical likelihood overcomes the challenges that the posterior distribution lacks a closed analytic form and its domain is nonconvex. We develop the leapfrog parameter tuning algorithm for Bayesian empirical likelihood. We also show that the posterior distributions of the regression coefficients are asymptotically normal. Simulation studies and real data analysis demonstrate the advantages of the proposed method in prediction accuracy.
