Assessing Joint Distributions with Isoprobability Contours

Authors:
Ali E. Abbas;David V. Budescu;Yuhong (Rola) Gu
Affiliations:
Department of Industrial and Enterprise Systems Engineering, College of Engineering, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801;Department of Psychology, Fordham University, Bronx, New York 10458/ and Department of Psychology, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801;Department of Psychology, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801
Venue:
Management Science
Year:
2010

Citing 5
Cited 0

Knowledge maps

Management Science
Estimating correlations from scatterplots

Human Factors
An application of copulas to accident precursor analysis

Management Science
Correlations and Copulas for Decision and Risk Analysis

Management Science
Assessing Dependence: Some Experimental Results

Management Science

Quantified Score

Hi-index	0.01

Visualization

Abstract

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.