Networks effectively capture interactions among components of complex systems, and have
thus become a mainstay in many scientific disciplines. Growing evidence, especially from …

Graphs representing complex systems often share a partial underlying structure across
domains while retaining individual features. Thus, identifying common structures can shed …

Estimation of density functions supported on general domains arises when the data are
naturally restricted to a proper subset of the real space. This problem is complicated by …

We propose a distributed bootstrap method for simultaneous inference on high-dimensional
massive data that are stored and processed with many machines. The method produces an …

Markov networks are frequently used in sciences to represent conditional independence
relationships underlying observed variables arising from a complex system. It is often of …

In this paper, we propose a bootstrap method applied to massive data processed
distributedly in a large number of machines. This new method is computationally efficient in …

We consider the problem of estimating the difference between two functional undirected
graphical models with shared structures. In many applications, data are naturally regarded …

In typical high dimensional statistical inference problems, confidence intervals and
hypothesis tests are performed for a low dimensional subset of model parameters under the …

Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has
become a popular tool for modeling the network of interactions among high-dimensional …

We consider the problem of estimating the difference between two undirected functional
graphical models with shared structures. In many applications, data are naturally regarded …