Methods for estimating sparse and large covariance matrices Covariance and correlation
matrices play fundamental roles in every aspect of the analysis of multivariate data collected …

Finding an unconstrained and statistically interpretable reparameterization of a covariance
matrix is still an open problem in statistics. Its solution is of central importance in covariance …

China has experienced profound economic and social changes in recent decades. During
this period, China's fiscal policy framework has been substantially reformed. The objective of …

The risk of a large portfolio is often estimated by substituting a good estimator of the volatility
matrix. However, the accuracy of such a risk estimator is largely unknown. We study factor …

The modified Cholesky decomposition is commonly used for precision matrix estimation
given a specified order of random variables. However, the order of variables is often not …

Motivation With the rapid development of modern technologies, massive data are available
for the systematic study of Alzheimer's disease (AD). Though many existing AD studies …

Instantaneous dependence among several asset returns is the main reason for the
computational and statistical complexities in working with full multivariate GARCH models …

Variable selection for structured covariates lying on an underlying known graph is a problem
motivated by practical applications, and has been a topic of increasing interest. However …

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes
across different cell types. To help decipher this complexity, we introduce single-cell …

Estimating time‐varying covariance matrices of the vector of interest is challenging both
computationally and statistically due to a large number of constrained parameters. In this …