In this monograph, we have attempted to summarize the actively developing field of
statistical learning with sparsity. A sparse statistical model is one having only a small …

The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …

The Gaussian graphical model (GGM) is an increasingly popular technique used in
psychology to characterize relationships among observed variables. These relationships are …

In the past decade, sparse and low-rank recovery has drawn much attention in many areas
such as signal/image processing, statistics, bioinformatics, and machine learning. To …

Gaussian graphical models provide a powerful framework to reveal the conditional
dependency structure between multivariate variables. The process of uncovering the …

A pressing challenge in single-cell transcriptomics is to benchmark experimental protocols
and computational methods. A solution is to use computational simulators, but existing …

A family of prior distributions for covariance matrices is studied. Members of the family
possess the attractive property of all standard deviation and correlation parameters being …

An important goal for psychological science is developing methods to characterize
relationships between variables. Customary approaches use structural equation models to …

An understanding of the spatial dimension of economic and social activity requires methods
that can separate out the relationship between spatial units that is due to the effect of …

Covariance matrix and its inverse, known as the precision matrix, have many applications in
multivariate analysis because their elements can exhibit the variance, correlation …