Model-Averaged [Formula: see text] Regularization using Markov Chain Monte Carlo Model Composition.
Model-Averaged [Formula: see text] Regularization using Markov Chain Monte Carlo Model Composition.
复制标题
模型平均 [公式：参见文本] 使用马尔可夫链蒙特卡罗模型组合进行正则化。
DOI：
10.1080/00949655.2013.861839
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发表时间：
2015
影响因子：
1.2
通讯作者：
Percival,Daniel
中科院分区：
文献类型：
--
作者：
Fraley,Chris;Percival,Daniel
贝叶斯模型平均（BMA）是解决变量选择问题中模型不确定性的有效技术。然而，当前的 BMA 方法在处理测量值（变量）多于样本的数据时存在计算困难。本文提出了一种将 ℓ1 正则化和马尔可夫链蒙特卡罗模型组合技术相结合的 BMA 方法。通过将 ℓ1 正则化路径视为模型空间，我们提出了一种方法来解决解决方案路径点选择的模型平均中出现的模型不确定性问题。我们证明该方法在计算和经验上对于高维数据集的回归和分类是有效的。我们将我们的技术应用于模拟以及基因组学中出现的一些应用。
Bayesian model averaging (BMA) is an effective technique for addressing model uncertainty in variable selection problems. However, current BMA approaches have computational difficulty dealing with data in which there are many more measurements (variables) than samples. This paper presents a method for combining ℓ1regularization and Markov chain Monte Carlo model composition techniques for BMA. By treating the ℓ1regularization path as a model space, we propose a method to resolve the model uncertainty issues arising in model averaging from solution path point selection. We show that this method is computationally and empirically effective for regression and classification in high-dimensional data sets. We apply our technique in simulations, as well as to some applications that arise in genomics.