Model Interpretation Through Lower-Dimensional Posterior Summarization
Model Interpretation Through Lower-Dimensional Posterior Summarization
复制标题
通过低维后验总结进行模型解释
DOI：
--
复制
发表时间：
2019
影响因子：
2.4
通讯作者：
Jared S. Murray
中科院分区：
文献类型：
--
作者：
S. Woody;C. Carvalho;Jared S. Murray
摘要随着数据集规模和复杂性的不断增加，非参数回归模型的能力和流行度也在不断提高。虽然这些模型已经证明了它们在经验环境中的预测能力，但它们通常难以解释，并且无法解决分析师或决策者的潜在推理目标。在本文中，我们提出了一种模块化的两阶段方法，用于创建复杂模型的简约、可解释的摘要，从而允许自由选择建模技术和推理目标。在第一阶段，一个灵活的模型是适合这被认为是尽可能准确的。在第二阶段，通过将分布中的绘制投影到更简单的结构上来构建低维摘要。这些总结自然带有有效的贝叶斯不确定性估计。此外，由于我们只使用一次数据从先验移动到后验，这些不确定性估计在多个摘要中以及迭代改进摘要后仍然有效。我们应用我们的方法，并在一系列模拟和真实的数据集上展示其优势。我们在这里介绍的方法是在一个R包中实现的，可以在github.com/spencerwoody/possum上找到。本文的补充材料可在网上查阅。
ABSTRACT Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven their predictive ability in empirical settings, they are often difficult to interpret and do not address the underlying inferential goals of the analyst or decision maker. In this article, we propose a modular two-stage approach for creating parsimonious, interpretable summaries of complex models which allow freedom in the choice of modeling technique and the inferential target. In the first stage, a flexible model is fit which is believed to be as accurate as possible. In the second stage, lower-dimensional summaries are constructed by projecting draws from the distribution onto simpler structures. These summaries naturally come with valid Bayesian uncertainty estimates. Further, since we use the data only once to move from prior to posterior, these uncertainty estimates remain valid across multiple summaries and after iteratively refining a summary. We apply our method and demonstrate its strengths across a range of simulated and real datasets. The methods we present here are implemented in an R package available at github.com/spencerwoody/possum. Supplementary materials for this article are available online.
DOI：
10.1002/cjs.11675
发表时间：
2019-03
期刊：
Canadian Journal of Statistics
影响因子：
--
作者：
Koji Miyawaki;S. MacEachern
通讯作者：
Koji Miyawaki;S. MacEachern