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SUMMARY:Testing Covariance Separability in High Dimensions
DTSTART:20260918T151500
DTEND:20260918T164500
DTSTAMP:20261002T093407Z
UID:e8e891121c9daa2717a128e26509127cd893c6017c82d817624dd662
CATEGORIES:Conferences - Seminars
DESCRIPTION:Tomas Masak\, Wirtschaftsuniversitaet Wien\, Austria\nSeparab
 ility is an important structural assumption often placed on the covariance
  when working with matrix-variate data\, because it greatly simplifies bot
 h interpretation and computation of subsequent covariance-based statistica
 l tasks. Yet testing the separability assumption is difficult in the high-
 dimensional regime.\nWe propose to test separability by recasting the prob
 lem as a sphericity test after whitening the data using the separable maxi
 mum 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.\n
 To reduce its reliance on distributional assumptions\, we introduce an ang
 ular version of the test based on radial normalization after whitening. We
  demonstrate the practical utility\, empirical power\, and computational e
 fficiency of the proposed tests in a large simulation study and on a real-
 world acoustic data set.
LOCATION:CM 1 517 https://plan.epfl.ch/?room==CM%201%20517
STATUS:CONFIRMED
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