Out-of-Sample Fusion
| dc.contributor.advisor | Kedem, Benjamin | en_US |
| dc.contributor.author | Zhou, Wen | en_US |
| dc.contributor.department | Mathematical Statistics | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2013-06-28T05:37:18Z | |
| dc.date.available | 2013-06-28T05:37:18Z | |
| dc.date.issued | 2013 | en_US |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1903/13981 | |
| dc.subject.pqcontrolled | Statistics | en_US |
| dc.subject.pquncontrolled | Confidence interval | en_US |
| dc.subject.pquncontrolled | Density Ratio Model | en_US |
| dc.subject.pquncontrolled | Out-of-Sample Fusion | en_US |
| dc.subject.pquncontrolled | Threshold Probability | en_US |
| dc.title | Out-of-Sample Fusion | en_US |
| dc.type | Dissertation | en_US |
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