BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Arts and Sciences,College of Engineering,Lectures and Seminars DESCRIPTION:We develop a variational Bayes approach to approximate the post erior distributions under the exponential tilted empirical likelihood fram ework for stationary time series data. Moment conditions are obtained usin g Whittle’s estimation method that transforms the original dependent obs ervations to approximately independent periodogram ordinates. Two reparame terization techniques are adopted to ensure the stationarity and invertibi lity of the parameters under the Markov Chain Monte Calo sampling and to f acilitate a more accurate posterior approximation under the variational Ba yes algorithm. A calibration technique is further proposed to guarantee th e posterior coverage probability attains the nominal level. Extensive simu lation studies and a real data analysis are conducted toinvestigate the em pirical performance of the proposed method. \nEvent page: ht tps://www.umassd.edu/events/cms/10-7-26-joint-dsccis-seminar-series---talk -by-dr-jing-wu-of-uri.php X-ALT-DESC;FMTTYPE=text/html:

We develop a variational Bayes approach to approximate the posterior distributions under the exponential tilted empirical likelihood framework for stationary time series data. Mom ent conditions are obtained using Whittle’s estimation method that trans forms the original dependent observations to approximately independent per iodogram ordinates. Two reparameterization techniques are adopted to ensur e the stationarity and invertibility of the parameters under the Markov Ch ain Monte Calo sampling and to facilitate a more accurate posterior approx imation under the variational Bayes algorithm. A calibration technique is further proposed to guarantee the posterior coverage probability attains t he nominal level. Extensive simulation studies and a real data analysis ar e conducted to
investigate the empirical performance of the proposed me thod.

Event p age: https://www.umassd.edu/events/c ms/10-7-26-joint-dsccis-seminar-series---talk-by-dr-jing-wu-of-uri.php

DTSTAMP:20260911T214244 DTSTART;TZID=America/New_York:20261007T013000 DTEND;TZID=America/New_York:20261007T150000 LOCATION:CCB-243 SUMMARY;LANGUAGE=en-us:Joint DSC/CIS seminar series - talk by Dr. Jing Wu o f URI UID:9c37ff051bff1af21577e78adeca74ee@www.umassd.edu END:VEVENT END:VCALENDAR