We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the...

Least squares multidimensional scaling (MDS) is a classical method for representing a nxn dissimilarity matrix . One seeks a set of configuration points such that is well approximated by the Euclidean distances between the configuration points: . Suppose that in addition to , a vector of...

In some multivariate problems with missing data, pairs of variables exist that are never observed together. For example, some modern biological tools can produce data of this form. As a result of this structure, the covariance matrix is only partially identifiable, and point estimation requires...