TITLE: Robust Association Studies using Density Power Divergences
SPEAKER: Professor T.N. Sriram
In this talk, we will introduce a family of multivariate association measures based on density power divergences that help recover both linear and nonlinear relationships between multiple sets of random vectors. This approach not only characterizes independence, but also provides a smooth bridge between well-known distances that are inherently robust against outliers. Algorithmic approaches are developed for dimension reduction and selection of the optimal robust association index. Extensive simulation studies are performed to assess the robustness of these association measures under different types and proportions of contamination. We illustrate the usefulness of our methods in applications. Some theoretical properties, including the consistency of the estimated coefficient vectors, are investigated and computationally efficient algorithms for our nonparametric methods are provided.