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METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20220503T232115Z
DTSTART:20220511T130000Z
DTEND:20220511T140000Z
SUMMARY:Emmanuel Ogundimu - Recent advances in sample selection models
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}zwn-ky48qus
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DESCRIPTION:Emmanuel Ogundimu\, Assistant Professor of Statistics in the
Department of Mathematical Sciences\, Durham University is our speaker f
or the Statistics seminar series.\n\nTitle: Recent advances in sample se
lection models\n\nAbstract: Sample selection arises when the outcome of
interest is partially observed in a study. In the presence of sample sel
ection\, the observed data is not representative of the original populat
ion\, even after controlling for explanatory variables. That is\, data a
re missing not at random and the usual assumption that missing data proc
ess is independent of the outcome given the observed variables is not te
nable. Although sophisticated statistical methods in the parametric\, se
miparametric\, and non-parametric framework have been proposed to solve
this problem\, classical estimators introduced by James Heckman remain p
opular. Apart from the fact that these estimators are very sensitive to
small deviations from the distributional assumptions\, which are often n
ot satisfied in practice\, there is a need for the so-called exclusion r
estriction. That is\, some of the variables affecting the missing data p
rocess do not affect the outcome. The drive to establish this requiremen
t often leads to the inclusion of irrelevant variables in the model.\n\n
In this talk\, I will motivate the problem of sample selection from non-
random treatment allocation perspective and discuss the general modellin
g framework. In particular\, the link between sample selection models an
d skew-distributions will be established. A new result on regularized sa
mple selection model and some open problems in this framework will concl
ude the talk.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:Zoom link: https://zoom.us/j/92947173491
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