A framework for measuring association of random vectors via collapsed random variables

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 …

Visualizing dependence in high-dimensional data. An application to S&P 500 constituent data

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?

Exploratory Visualization of Higher Dimensional Data

Research seminar given at the Institute for Statistics and Mathematics, Wien University.

About "her emails"

Patterns in Secretary Clinton's emails and a website (select "Code") that allows anyone to interactively explore the patterns.

Illuminant estimation using ensembles of multivariate regression trees

In this paper, we show that a simple and accurate ensemble model can be learned by (i) using multivariate regression trees to take into account that the chromaticity components of the illuminant are correlated and constrained, and (ii) fitting each tree by directly minimizing a loss function of interest—such as recovery angular error or reproduction angular error—rather than indirectly using the squared-error loss function as a surrogate. We show empirically that overall our method leads to improved performance on diverse image sets.

Two-stage Approach for Unbalanced Classification with Time-varying Decision Boundary: Application to Marine Container Inspection

Two million marine containers arrive each year at Canadian ports, representing a significant percentage of Canada's trade with its overseas partners. While the majority of these commercial shipments are perfectly legitimate, some marine containers …