Testing Covariance Separability in High Dimensions

Speaker: Tomas Masak, Wirtschaftsuniversitaet Wien, Austria Organiser: Victor Panaretos <p>Separability is an important structural assumption often placed on the covariance when working with matrix-variate data, because it greatly simplifies both interpretation and computation of subsequent covariance-based statistical tasks. Yet testing the separability assumption is difficult in the high-dimensional regime.<br> We propose to test separability by recasting the problem as a sphericity test after whitening the data using the separable maximum likelihood estimate of the covariance. The test is calibrated by Monte Carlo simulation, yielding finite-sample level control. Furthermore,