We study the multivariate square-root lasso, a method for fitting the multivariate response
linear regression model with dependent errors. This estimator minimizes the nuclear norm of …

Estimating the unconstrained mean and covariance matrix is a popular topic in statistics.
However, estimation of the parameters of N p (μ, Σ) under joint constraints such as Σ μ= μ …

Estimation of a covariance matrix or its inverse, the precision matrix, is fundamental to
multivariate analysis. Although the covariance matrix is more familiar to practitioners and …

Estimation of the mean vector and covariance matrix is of central importance in the analysis
of multivariate data. In the framework of generalized linear models, usually the variances are …

We study estimation of the mean-covariance under the joint constraint Σ μ= μ for a
multivariate normal. A reparametrized structured covariance is proposed through spectral …