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.