B1382
Title: Statistical data depth and its applications to health sciences
Authors: Sara Lopez Pintado - Northeastern University (United States) [presenting]
Abstract: Data depth was originally introduced for multivariate data as a powerful non-parametric tool for developing robust exploratory data analysis methods. It provides a way of measuring how representative an observation is within the distribution or sample and of ranking multivariate observations from center-outward. Based on these depth-rankings, robust estimators and outliers can be defined. Notions of depth have been extended to functional data in the last several decades. We develop different depth-based methods for general functional data, such as an envelope test for detecting and visualizing differences between groups of functions. We applied this method to longitudinal growth data, where the goal is to find differences between the growth pattern of normal versus premature low birth weight babies. We also introduce and establish the properties of the metric halfspace depth, an extension of the well-known Tukey's depth to object data in general metric spaces. The metric halfspace depth was applied to an Alzheimer's disease study, revealing group differences in the brain connectivity, modeled as covariance matrices, for subjects in different stages of dementia.
