• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >Confidence graphs for graphical model selection
【24h】

Confidence graphs for graphical model selection

机译：图形模型选择的信心图

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this article, we introduce the concept of confidence graphs (CG) for graphical model selection. CG first identifies two nested graphical models-called small and large confidence graphs (SCG and LCG)-trapping the true graphical model in between at a given level of confidence, just like the endpoints of traditional confidence interval capturing the population parameter. Therefore, SCG and LCG provide us with more insights about the simplest and most complex forms of dependence structure the true model can possibly be, and their difference also offers us a measure of model selection uncertainty. In addition, rather than relying on a single selected model, CG consists of a group of graphical models between SCG and LCG as the candidates. The proposed method can be coupled with many popular model selection methods, making it an ideal tool for comparing model selection uncertainty as well as measuring reproducibility. We also propose a new residual bootstrap procedure for graphical model settings to approximate the sampling distribution of the selected models and to obtain CG. To visualize the distribution of selected models and its associated uncertainty, we further develop new graphical tools, such as grouped model selection distribution plot. Numerical studies further illustrate the advantages of the proposed method.
机译：在本文中，我们介绍了用于图形模型选择的置信图（CG）的概念。 CG首先识别两个嵌套的图形模型 - 称为小而大的置信图（SCG和LCG） - 在给定的置信度级别之间的真实图形模型，就像传统置信区间的终点捕获人口参数。因此，SCG和LCG为我们提供了对最简单和最复杂形式的依赖结构的见解，真实的模型可能是，它们的差异也为我们提供了模型选择不确定性的衡量标准。另外，而不是依赖于单个所选模型，CG由SCG和LCG之间的一组图形模型作为候选者组成。所提出的方法可以与许多流行的模型选择方法耦合，使其成为比较模型选择不确定性的理想工具，以及测量再现性。我们还提出了一种新的残余引导程序，用于图形模型设置，以近似所选模型的采样分布并获得CG。为了可视化所选模型的分布及其相关的不确定性，我们进一步开发了新的图形工具，例如分组的模型选择分布图。数值研究进一步说明了所提出的方法的优点。

著录项

  • 来源
    《Statistics and computing》 |2021年第5期|52.1-52.21|共21页
  • 作者

    Wang Linna; Qin Yichen; Li Yang;

  • 作者单位

    Univ Cincinnati Dept Math Sci Cincinnati OH USA;

    Univ Cincinnati Dept Operat Business Analyt & Informat Syst Cincinnati OH 45221 USA;

    Renmin Univ China Ctr Appl Stat Beijing Peoples R China|Renmin Univ China Sch Stat Beijing Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bootstrap; Confidence set; Model selection uncertainty;

    机译：举止;信心集;模型选择不确定性;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号