Browsing by Subject "Empirical Bayes"

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    Empirical Bayes Identification of High Hazard Locations for Older Drivers
    (Minnesota Department of Transportation, 1994-10) Davis, Gary A.
    As part of an emphasis on improving road safety, the Minnesota Department of Transportation seeks to identify the locations where older drivers were over-represented in accident records. This research project reports on the use of three methods to help improve the accuracy of identifying locations where older drivers were at increased risk: a basic statistical model, the Empirical Bayes statistical method and a clustering method. Overall, the basic statistical model preformed the best. The clustering method and the Empirical Bayes method could both be usefully applied to the traditional task of high-hazard identification--that of automatically screening a large number of accident sites to identify potential candidates for improvement. This information can point the way to areas that may require a more detailed engineering analysis.
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    Statistical methods for multi-class differential gene expression detection
    (2011-11) Cao, Xiting
    One of the major goals of microarray data analysis is to identify differentially expressed genes. In cancer studies, RNA is extracted from the tissue samples of cancer patients (case class) and healthy people (control class) to obtain the gene expression data and genes that are dierentially expressed between case and control are identied to be candidate biomarkers which could undergo further studies. More often, we encounter situations where gene expression between more than two classes are being compared instead of the traditional case/control setup, e.g., multiple disease stages or dierent experimental conditions. In this dissertation, the problem of identifying dierentially expressed genes in a multi-class comparison setting will be addressed. To identify the dierentially expressed genes, it is important to select a test statistic to rank the genes, and common approaches usually summarize each gene expression into a univariate test statistic and nd a critical value for the ranking statistics to claim which gene is dierentially expressed. In the dissertation, a univariate test statistic (the moderated F-statistics) is rst used as a summary statistic and its distribution is empirically estimated using maximum likelihood. After that, A multivariate test statistic is proposed as a summary statistic for each gene and both parametric and non-parametric empirical Bayes approaches are adopted to rank the genes. The performances of the proposed methods are illustrated by extensive simulation studies and application to public microarray datasets. The results show that the proposed methods have better detection power than the commonly used approaches when controlling false discovery rates at the same level.