A framework for quantifying dependence between random vectors is introduced. Using the notion of a collapsing function, random vectors are summarized by single random variables, referred to as collapsed random variables. Measures of association …

Motivated by the use of high-dimensional data such as data from several hundred risk-factor changes in the realm of quantitative risk management, we raise the following simple question, namely, How can one detect and visualize dependence in high-dimensional data?

The paper introduces a special case of the Euclidean distance matrix completion problem of interest in statistical data analysis where only the minimal spanning tree distances are given and the matrix completion must preserve the minimal spanning tree. A guided random search algorithm is shown to outperform more standard optimization methods which also force peculiar and generally unwanted geometric structure on the point configurations their completions produce.

The structure of a set of high dimensional data objects (e.g. images, documents, molecules, genetic expressions, etc.) is notoriously difficult to visualize. In contrast, lower dimensional structures (esp. 3 or fewer dimensions) are natural to us and …

We propose using graph theoretic results to develop an infrastructure that tracks movement from a display of one set of variables to another. The illustrative example throughout is the real-time morphing of one scatterplot into another. Hurley and …

We propose using graph theoretic results to develop an infrastructure that tracks movement from a display of one set of variables to another. The illustrative example throughout is the real-time morphing of one scatterplot into another. Hurley and …

A graph-theoretic approach is taken to the component order problem in the layout of statistical graphics. Eulerian tours and Hamiltonian decompositions of complete graphs are used to ameliorate order effects in statistical graphics. Similarly, …

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