In the most recent years, an extraordinary research effort has emerged to disentangle
osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic …

Object oriented data analysis is the statistical analysis of populations of complex objects. In
the special case of functional data analysis, these data objects are curves, where a variety of …

New representations of tree-structured data objects, using ideas from topological data
analysis, enable improved statistical analyses of a population of brain artery trees. A number …

Integrative analysis of disparate data blocks measured on a common set of experimental
subjects is a major challenge in modern data analysis. This data structure naturally …

Objective Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of
potentially distinct phenotypes. The purpose of this study was to apply innovative machine …

In this paper, we study distance covariance, Hilbert–Schmidt covariance (aka Hilbert–
Schmidt independence criterion [In Advances in Neural Information Processing Systems …

We develop a projected Wasserstein distance for the two-sample test, a fundamental
problem in statistics and machine learning: given two sets of samples, to determine whether …

Peter Hall's work illuminated many aspects of statistical thought, some of which are very well
known including the bootstrap and smoothing. However, he also explored many other lesser …

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research
through new terminology for discussing the often many possible approaches to the analysis …

Brain functional connectivity has been studied by analyzing time series correlations in
regional brain activities based on resting-state fMRI data. Brain functional connectivity can …