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.
Quantile–quantile plots, or qqplots, are an important visual tool for many applications but their interpretation requires some care and often more experience. This apparent subjectivity is unnecessary. By drawing on the computational and display …