Partially Observed Dynamic Tensor Response Regression.

In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable.

Generalized Connectivity Matrix Response Regression with Applications in Brain Connectivity Studies.

Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose a new generalized matrix response regression model, where the observed network is treated as a matrix-valued response and the subject covariates as predictors. The new model characterizes the population-level connectivity pattern through a low-rank intercept matrix, and the effect of subject covariates through a sparse slope tensor.

Provable Convex Co-clustering of Tensors.

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations.

Tensor Graphical Model: Non-Convex Optimization and Statistical Inference.

We consider the estimation and inference of graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kronecker product structure. A critical challenge in the estimation and inference of this model is the fact that its penalized maximum likelihood estimation involves minimizing a non-convex objective function.

Simultaneous Clustering and Estimation of Heterogeneous Graphical Models.

We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical models. This is achieved via a high dimensional version of Expectation Conditional Maximization (ECM) algorithm (Meng and Rubin, 1993).

Evidence for preoperative aspirin improving major outcomes in patients with chronic kidney disease undergoing cardiac surgery: a cohort study.

Background: Effects of aspirin on patients with chronic kidney disease (CKD) remains unclear. This study aimed to examine the effect of preoperative aspirin use on postoperative renal function and 30-day mortality in patients with CKD undergoing cardiac surgery.

Methods: A retrospective cohort study was performed on consecutive patients (n = 5175) receiving cardiac surgery in 2 tertiary hospitals.

Preoperative aspirin use and outcomes in cardiac surgery patients.

Background: The effects of preoperative aspirin use on outcomes of cardiac surgery patients remain uncertain. This study was aimed to evaluate the effect of preoperative aspirin use on major outcomes in cardiac surgery patients.

Methods: An observational cohort study was performed on consecutive patients (n = 4256) undergoing cardiac surgery in 2 tertiary hospitals.