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authorStratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>2012-11-04 23:38:51 -0800
committerStratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>2012-11-04 23:38:51 -0800
commit4c6565a07b271974056dd2babd08ee26d82be1ef (patch)
treed19cd27314a11f25e4654f5beeb13456ba01b1c5
parentcec9b298f548ce44fe5eaecf71c04d65c95de993 (diff)
downloadrecommendation-4c6565a07b271974056dd2babd08ee26d82be1ef.tar.gz
muthu untro
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@@ -5,13 +5,13 @@ known to the experimenter.
\E\ wishes to perform an experiment that measures a certain inherent property of the subjects: the outcome for a subject $i$ is denoted $y_i$, which is unknown to \E\ before the experiment is performed.
Typically, \E\ has a hypothesis of the relationship between $x_i$'s and $y_i$'s, such as, say linear, \emph{i.e.}, $y_i \approx \T{\beta} x_i$; conducting the experiments and obtaining the measurements $y_i$ lets \E\ derive an estimate $\beta$. %The goal of experimental design amounts to determining which subjects to experiment upon to produce the best possible such estimate.
-The above experimental design scenario above has many applications, from medical testing to marketing research and others.
-There is a rich literature about various estimation, as well as for means for quantifying the quality of the produced estimate \cite{pukelsheim2006optimal}. There is also an extensive theory on how to select subjects
+The above experimental design scenario has many applications, from medical testing to marketing research and others.
+There is a rich literature about estimation procedures, as well as for means for quantifying the quality of the produced estimate \cite{pukelsheim2006optimal}. There is also an extensive theory on how to select subjects
if \E\ can conduct only a limited number of experiments, so the estimation process returns $\beta$
that approximates the true parameter of the underlying population \cite{ginebra2007measure,le1996comparison,chaloner1995bayesian,boyd2004convex}.
We depart from this classical set up by viewing experimental design in a strategic setting, and by studying mechanism design issues.
-In our setup, experiments cannot be manipulated and hence measurements are considered precise.\footnote{Thus, the experiments of our interest are statistically significant, ones where each experiment provides a reliable outcome.} However, there
+In our setup, experiments cannot be manipulated and hence measurements are considered precise.\footnote{Thus, the experiments of our interest are statistically significant ones where each experiment provides a reliable outcome.} However, there
is a cost $c_i$ associated with experimenting on
subject $i$ which varies from subject to subject. This may be viewed as the
cost subject $i$ incurs when tested, and hence $i$ needs to be reimbursed; or, it might be viewed as the incentive for $i$