Out-of-Sample Fusion
Out-of-Sample Fusion
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Date
2013
Authors
Zhou, Wen
Advisor
Kedem, Benjamin
Citation
DRUM DOI
Abstract
A novel method, called ``out-of-sample fusion", is proposed in this
dissertation. This method utilizes artificial samples along with a real data
sample of interest to draw statistical inference assuming a semiparametric
density ratio model. These artificial samples do not relate directly to the
sample of interest, which differentiates the method from the traditional
bootstrap approach which is a ``within-sample'' method. Out-of-sample fusion
has been elaborated on through the estimation of threshold probabilities and
their confidence intervals. A comparison has
been made with the Agresti-Coull and the standard Wald methods in
terms of confidence interval estimation. The out-of-sample fusion generates
sharper and shorter confidence intervals while the nominal coverage is
maintained. The out-of-sample method has been applied to cancer and
microarray data. An R package has been developed to facilitate the
implementation of the out-of-sample fusion method.