Brain structural networks are often represented as discrete adjacency
ma...
There is increasing interest in modeling high-dimensional longitudinal
o...
We propose a novel nonparametric model for diffusion MRI signal in q-spa...
With distinct advantages in power over behavioral phenotypes, brain imag...
The brain structural connectome is generated by a collection of white ma...
This work considers a continuous framework to characterize the
populatio...
It has become routine in neuroscience studies to measure brain networks ...
Vector autoregressions have been widely used for modeling and analysis o...
Manifold-valued functional data analysis (FDA) recently becomes an activ...
High angular resolution diffusion imaging (HARDI), a type of diffusion
m...
This paper develops a novel spatial quantile function-on-scalar regressi...
We propose a multivariate functional responses low rank regression model...
In brain connectomics, the cortical surface is parcellated into differen...
Our interest focuses on developing statistical methods for analysis of b...
Statistical methods relating tensor predictors to scalar outcomes in a
r...
Modern neuroimaging technologies, combined with state-of-the-art data
pr...
This paper studies change-points in human brain functional connectivity ...
Human brain functional connectivity (FC) is often measured as the simila...
Analysis of structural and functional connectivity (FC) of human brains ...
Advanced brain imaging techniques make it possible to measure individual...
While spherical data arises in many contexts, including in directional
s...
There is increasing interest in learning a set of small outcome-relevant...
This article focuses on the problem of studying shared- and
individual-s...
In machine learning it is common to interpret each data point as a vecto...
Unsupervised clustering of curves according to their shapes is an import...
Statistical classification of actions in videos is mostly performed by
e...