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UID:20250918T092106EDT-3698oaVf6h@132.216.98.100
DTSTAMP:20250918T132106Z
DESCRIPTION:Quebec Mathematics Sciences Colloquium\n\nTitle: Auto-regressiv
 e approximations to non-stationary time series\, with inference and applic
 ations (2023 CRM-SSC Prize Lecture)\n\nAbstract\n	Understanding the time-va
 rying structure of complex temporal systems is one of the main challenges 
 of modern time series analysis. In this talk\, I will demonstrate that a w
 ide range of short-range dependent non-stationary and nonlinear time serie
 s can be well approximated globally by a white-noise-driven auto-regressiv
 e (AR) process of slowly diverging order. Uniform statistical inference of
  the latter AR structure will be discussed through a class of high-dimensi
 onal L2 tests. I will further discuss applications of the AR approximation
  theory to globally optimal short-term forecasting\, efficient estimation\
 , and resampling inference under complex temporal dynamics.\n\nthe CRM\, R
 oom 6214\, Pavillon André-Aisenstadt \n\nUniversité de Montréal\n\nThe pre
 sentation will also be accessible using Zoom with the following informatio
 n:\n\n\n	Meeting ID: 842 2670 1306\,\n	Passcode: 692788.\n\n
DTSTART:20231006T193000Z
DTEND:20231006T203000Z
SUMMARY:Zhou Zhou (University of Toronto)
URL:/mathstat/channels/event/zhou-zhou-university-toro
 nto-351596
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