BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
PRODID:-//Missouri State University/Calendar of Events//EN
CALSCALE:GREGORIAN
X-WR-TIMEZONE:America/Chicago
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
DTSTART:20070311T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
TZNAME:CDT
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
DTSTART:20071104T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
TZNAME:CST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:65d1aa52-d985-4dd0-8c43-137f787753fe.211076@calendar.missouristate.edu
CREATED:20201102T145738Z
LAST-MODIFIED:20201102T145738Z
LOCATION:
SUMMARY:Mathematic Colloquium via Zoom
DESCRIPTION:Sufficient Variable Selection and Dimension Reduction via Expe
 cted Conditional Hilbert-Schmidt Independence Criterion\n\n\n\n\n\nDr. Ch
 enlu Ke\n\n\nVirginia Commonwealth University\n\n\nAbstract\n\n\nIn this 
 talk\, we first introduce a model-free sufficient variable screening proc
 edure for ultrahigh dimensional data based on a newly developed independe
 nce measure. Compared with sure independence screening and its family\, o
 ur approach inherits the power of the new measure and incorporates joint 
 information between variables additionally to achieve sufficient variable
  screening. The advantages of our method are illustrated theoretically an
 d numerically. We then introduce a novel sufficient dimension reduction a
 pproach using the same independence measure. An algorithm is developed to
  search dimension reduction directions using sequential quadratic program
 ming. The method can be applied after our variable selection procedure to
  further extract information from data. A real data example is presented 
 to demonstrate the joint use of the two methods.\n\n\n\n\n\nZoom ID: 967 
 4863 5043. Passcode upon request.
X-ALT-DESC;FMTTYPE=text/html:&lt;html&gt;&lt;head&gt;&lt;title&gt;&lt;/title&gt;&lt;/head&gt;&lt;body&gt;&lt;p&gt;&lt;s
 trong&gt;Sufficient Variable Selection and Dimension Reduction via Expected 
 Conditional Hilbert-Schmidt Independence Criterion&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;/p&gt;\
 n&lt;p&gt;&lt;strong&gt;Dr. Chenlu Ke&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Virginia Commonwealth 
 University&lt;/strong&gt;&lt;/p&gt;\n&lt;p&gt;&lt;em&gt;Abstract&lt;/em&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;In this talk\
 , we first introduce&amp;nbsp\;a model-free sufficient variable screening pro
 cedure for ultrahigh dimensional data based on a newly developed independ
 ence measure. Compared with sure independence&amp;nbsp\;screening and its fam
 ily\,&amp;nbsp\;our approach inherits the power of the new measure and incorp
 orates joint information between variables additionally to achieve suffic
 ient variable screening.&amp;nbsp\;The advantages of our method are illustrat
 ed theoretically and numerically. We then introduce a novel sufficient di
 mension reduction approach using the same independence measure.&amp;nbsp\;An 
 algorithm is developed to search dimension reduction directions using seq
 uential quadratic programming. The method can be applied after our variab
 le selection procedure to further extract information from data. A real d
 ata example is presented to demonstrate the joint use of the two methods.
 &lt;/span&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;&lt;span&gt;Zoom ID:&amp;nbsp\;&lt;/spa
 n&gt;967 4863 5043. Passcode upon request.&lt;/p&gt;&lt;/body&gt;&lt;/html&gt;
DTSTART;TZID=America/Chicago:20201110T150000
DTEND;TZID=America/Chicago:20201110T160000
SEQUENCE:0
URL:
CATEGORIES:Public,Alumni,Current Students,Faculty,Staff
END:VEVENT
END:VCALENDAR