摘要翻译：
我们提出了一个用户友好的图形工具，半盘密度条(HDDS)，用于可视化和比较概率密度函数。HDDS利用颜色阴影以直观的方式表示分布。在单变量设置中，半盘密度条允许立即识别密度的关键特征，如对称性、分散性和多模态。在多变量设置中，我们定义了HDDS表来推广列联表的概念。它是一个半圆盘密度条的阵列，它紧凑地显示了一个感兴趣变量的单变量边缘密度和条件密度，以及条件变量的联合密度和边缘密度。此外，HDDSs的结构非常适合于容易比较密度对。为了突出所提方法的具体好处，我们展示了如何使用HDDSs从调查数据中分析收入分配和生活满意度，有条件地进行连续和分类控制。实现HDDS方法的代码通过一个专用的R包提供。
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英文标题：
《Visualizing and comparing distributions with half-disk density strips》
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作者：
Carlo Romano Marcello Alessandro Santagiustina and Matteo Iacopini
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  We propose a user-friendly graphical tool, the half-disk density strip (HDDS), for visualizing and comparing probability density functions. The HDDS exploits color shading for representing a distribution in an intuitive way. In univariate settings, the half-disk density strip allows to immediately discern the key characteristics of a density, such as symmetry, dispersion, and multi-modality. In the multivariate settings, we define HDDS tables to generalize the concept of contingency tables. It is an array of half-disk density strips, which compactly displays the univariate marginal and conditional densities of a variable of interest, together with the joint and marginal densities of the conditioning variables. Moreover, HDDSs are by construction well suited to easily compare pairs of densities. To highlight the concrete benefits of the proposed methods, we show how to use HDDSs for analyzing income distribution and life-satisfaction, conditionally on continuous and categorical controls, from survey data. The code for implementing HDDS methods is made available through a dedicated R package.
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PDF链接：
https://arxiv.org/pdf/2006.16063