The Markov property is widely imposed in analysis of time series data.
C...
There is increasing interest in modeling high-dimensional longitudinal
o...
A brain-computer interface (BCI) is a technology that enables direct
com...
In this article, we propose a novel pessimism-based Bayesian learning me...
In this article, we propose a general nonlinear sufficient dimension
red...
Two-sample multiple testing problems of sparse spatial data are frequent...
Ordinary differential equation (ODE) is an important tool to study the
d...
In this article, we propose a new hypothesis testing method for directed...
Thanks to its fine balance between model flexibility and interpretabilit...
Learning of matrix-valued data has recently surged in a range of scienti...
Motivated by an imaging proteomics study for Alzheimer's disease (AD), i...
Multimodal imaging has transformed neuroscience research. While it prese...
High-dimensional vector autoregression with measurement error is frequen...
Multimodal data are now prevailing in scientific research. A central que...
Ordinary differential equations (ODE) are widely used in modeling biolog...
A research topic of central interest in neuroimaging analysis is to stud...
In this article, we consider the problem of high-dimensional conditional...
Mediation analysis is becoming an increasingly important tool in scienti...
In modern data science, dynamic tensor data is prevailing in numerous
ap...
Point process modeling is gaining increasing attention, as point process...
Multimodal data, where different types of data are collected from the sa...
With fast advancements in technologies, the collection of multiple types...
Inferring brain connectivity network and quantifying the significance of...
Comparing two population means of network data is of paramount importanc...
With the widespread success of deep neural networks in science and
techn...
We consider the problem of decomposition of multiway tensor with binary
...
Multiple-network data are fast emerging in recent years, where a separat...
Time-varying networks are fast emerging in a wide range of scientific an...
Dynamic tensor data are becoming prevalent in numerous applications. Exi...
Motivated by applications in neuroimaging analysis, we propose a new
reg...