Dear all,
On Thursday 1st October, at 12:00 in room A105, the Department of AI, Data and Decision Sciences of Luiss University of Rome will host a seminar by
Tomas Masak (WU Wien)
Title: "Testing Covariance Separability in High Dimensions”
Abstract: 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. 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, we prove the test's high-dimensional consistency under dense alternatives. To reduce its reliance on distributional assumptions, we introduce an angular version
of the test based on radial normalization after whitening. We demonstrate the practical utility, empirical power, and computational efficiency of the proposed tests in a large simulation study and on a real-world acoustic data set.
Anyone interested in attending is welcome; just email me for logistical purposes.
Hoping to meet you there!
Best,
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