BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//MIDAS - ECPv4.9.3.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:MIDAS
X-ORIGINAL-URL:https://midas.umich.edu
X-WR-CALDESC:Events for MIDAS
BEGIN:VEVENT
DTSTART;TZID=UTC+0:20151029T153000
DTEND;TZID=UTC+0:20151029T170000
DTSTAMP:20191215T055050
CREATED:20160316T231613Z
LAST-MODIFIED:20160316T231615Z
UID:16631-1446132600-1446138000@midas.umich.edu
SUMMARY:Biostatistics Seminar: Donald Rubin\, PhD (Harvard University)
DESCRIPTION:Title: Balanced 2^K Factorial Experiments and ReRandomization for Increased Precision Abstract: The topic of “Big Data” in the context of randomized experiments\, suggests many meanings of “big”: A large number of treatment combinations under study\, as in a balanced 2^K factorial experiment with large K; a large number of background covariates available on the experimental units\, whose distributions randomization is “expected” to balance across all treatments; a large number of outcome variables of varying interest to investigators; and as a result\, a potentially large number of questions being addressed by an analysis of the resulting data\, typically using many statistical tests of linear-model factorial effects (i.e.\, main effects\, interactions). The history of 2^K factorial experiments is long (e.g.\, Fisher\, 1942; Yates\, 1937) and includes many innovative contributions. This brief presentation will focus on one previously unstudied aspect using extensions of recent formal statistical work by Morgan and Rubin (2008 Annals of Statistics; 2015 JASA) that relies on modern computing to implement: Selecting one particular randomized allocation by re-randomizing until “acceptable” covariate balance is found with respect to estimates of all factorial effects. This is joint work with Tirthankar Dasgupta and Zach Branson. \n
URL:https://midas.umich.edu/event/biostatistics-seminar-donald-rubin-phd-harvard-university-3/
END:VEVENT
END:VCALENDAR