BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//UM//UM*Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20210924T104552
DTSTART;TZID=America/Detroit:20211001T100000
DTEND;TZID=America/Detroit:20211001T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Xiaotong Shen\, John Black Johnston Distinguished Professor\, School of Statistics\, University of Minnesota
DESCRIPTION:Abstract: Inference of multiple directed relations between primary variables presents challenges in the presence of unspecified interventions. In this presentation\, we focus on the problem of inferring multiple directed relations simultaneously while identifying unspecified interventions. First\, we derive conditions to yield an identifiable model. Then\, we propose constrained regressions for causal discovery to identify the ancestral relations in addition to the instrument interventions for each hypothesis-specific primary variable\, eliminating nuisance parameters for hypothesis testing. On this ground\, we propose a modified likelihood ratio based on data perturbation to account for the identification effect by perturbing original data to assess the uncertainty associated with identifying ancestors and interventions. For testing the presence and strengths of parent-child relations in a pathway\, we show that the proposed tests achieve desired statistical properties. Finally\, numerical examples will be given to demonstrate the utility and effectiveness of the proposed procedure.\n\nThis work is joint with Chunlin Li and Wei Pan at the University of Minnesota.\n\n\nXiaotong T. Shen is the John Black Johnston Distinguished Professor in the College of Liberal Arts at the University of Minnesota. His areas of interest include machine learning and data mining\, high-dimensional analysis\, graphical models\, large margin methods\, personalization\, recommender systems\, natural language processing and text mining\, and nonconvex minimization. His current research effort is devoted to the further development of structured learning\, collabrative learning\, and scalable analysis. The targeted application areas are biomedical sciences\, artificial intelligence\, and engineering.\n\nhttps://dsmma.umn.edu/xiaotong-shen
UID:84418-21642773@events.umich.edu
URL:https://events.umich.edu/event/84418
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:Off Campus Location
CONTACT:
END:VEVENT
END:VCALENDAR