Assessing Joint Distributions with Isoprobability Contours
提出一种用等概率轮廓构建连续随机变量联合分布的新方法，将联合概率评估简化为一系列二元选择，无需直接评估变量间的相关性，并用体重和身高的第50百分位等概率轮廓数据验证了该方法。
We present a new method for constructing joint probability distributions of continuous random variables using isoprobability contours—sets of points with the same joint cumulative probability. This approach reduces the joint probability assessment into a one-dimensional cumulative probability assessment using a sequence of binary choices between various combinations of the variables of interest. The approach eliminates the need to assess directly the dependence, or association, between the variables. We discuss properties of isoprobability contours and methods for their assessment in practice. We also report results of a study in which subjects assessed the 50th percentile isoprobability contour of the joint distribution of weight and height. We use the data to show how to use the assessed contours to construct the joint distribution and to infer (indirectly) the dependence between the variables.