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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:A superpopulation treatment to case-control data a
nalysis - Yanyuan Ma (Pennsylvania State Universit
y)
DTSTART;TZID=Europe/London:20180619T110000
DTEND;TZID=Europe/London:20180619T120000
UID:TALK107302AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/107302
DESCRIPTION:We study the regression relationship among covaria
tes in case-control data\, an area known as the se
condary analysis of case-control studies. The cont
ext is such that only the form of the regression m
ean is specified\, so that we allow an arbitrary r
egression error distribution\, which can depend on
the covariates and thus can be heteroscedastic. U
nder mild regularity conditions we establish the t
heoretical identifiability of such models. Previou
s work in this context has either (a) specified a
fully parametric distribution for the regression e
rrors\, (b) specified a homoscedastic distribution
for the regression errors\, (c) has specified the
rate of disease in the population (we refer this
as true population)\, or (d) has made a rare disea
se approximation. We construct a class of semipara
metric estimation procedures that rely on none of
these. The estimators differ from the usual semipa
rametric ones in that they draw conclusions about
the true population\, while technically operating
in a hypothetic superpopulation. We also construct
estimators with a unique feature\, in that they a
re robust against the misspecification of the regr
ession error distribution in terms of variance str
ucture\, while all other nonparametric effects are
estimated despite of the biased samples. We estab
lish the asymptotic properties of the estimators a
nd \;illustrate their finite sample performanc
e through simulation studies.

LOCATION:Seminar Room 2\, Newton Institute
CONTACT:INI IT
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