B1107
Title: Two-sample test for multivariate activity densities evaluated from wearable devices over repeated assessments
Authors: Haochang Shou - University of Pennsylvania (United States) [presenting]
Abstract: Repeated observations have become increasingly common in biomedical research and longitudinal studies. For instance, wearable sensor devices are deployed to continuously track physiological and biological signals from each individual over multiple days. It remains of great interest to appropriately evaluate how the daily distribution of biosignals might differ across disease groups and demographics. Hence, the data could be formulated as multivariate complex object data such as probability densities, histograms, and observations on a tree. Traditional statistical methods often fail to apply as they are sampled from an arbitrary non-Euclidean metric space. A novel non-parametric graph-based, two-sample tests are proposed for object data with the same structure of repeated measures. A set of test statistics are proposed to capture various possible alternatives. The asymptotic null distributions are derived under the permutation null. The tests exhibit substantial power improvements over the existing methods while controlling the type I errors under finite samples, as shown through simulation studies. The proposed tests are demonstrated to provide additional insights into the location and inter- and intra-individual variability of the daily physical activity distributions in a sample of studies for mood disorders.
