Given a sample of i.i.d. high-dimensional centered random vectors, we consider a problem of estimation of their covariance matrix Σ with an additional assumption that Σ can be represented as a sum of a few Kronecker products of smaller matrices. Under mild conditions, we derive the first non-asymptotic dimension-free high-probability bound on the Frobenius distance between Σ and a widely used penalized permuted least squares estimate. Because of the hidden structure, the established rate of convergence is faster than in the standard covariance estimation problem.
No takes yet. Share an insight, caveat, or question.
Puchkin et al. (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: