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DTSTART;TZID=America/New_York:20261016T120000
DTEND;TZID=America/New_York:20261016T130000
DTSTAMP:20261013T183428Z
CREATED:20260729T193117Z
LAST-MODIFIED:20261013T183428Z
UID:10004002-1792152000-1792155600@live-hu-cmsa-222.pantheonsite.io
SUMMARY:An Introduction to Statistical Inference for Partially Identified Models
DESCRIPTION:Member Seminar \nSpeaker: Yiqi Liu \nTitle: An Introduction to Statistical Inference for Partially Identified Models \nAbstract: Classical statistical inference often relies on asymptotic approximations that are valid for each fixed data-generating process but may fail to hold uniformly over a class of relevant distributions. Starting with the familiar t-test\, I will illustrate why this distinction matters and discuss the additional challenges that arise when the parameter of interest is only partially identified\, i.e.\, when the observed data and maintained assumptions do not determine a unique parameter value.
URL:https://live-hu-cmsa-222.pantheonsite.io/event/member-seminar-101626/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://live-hu-cmsa-222.pantheonsite.io/media/2026/07/CMSA-Member-Seminar-10.16.26.docx-scaled.png
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