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    Multiple Testing in Grouped Dependent Data

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    Genre
    Thesis/Dissertation
    Date
    2013
    Author
    Clements, Nicolle
    Advisor
    Sarkar, S. K. (Sanat K.)
    Committee member
    Zhao, Zhigen
    Wei, William W. S.
    Heiberger, Richard M., 1945-
    Obradovic, Zoran
    Department
    Statistics
    Subject
    Statistics
    Astronomy
    False Discovery Rate
    Fdr
    Mdfdr
    Multiple Testing
    Ndvi
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2701
    
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    DOI
    http://dx.doi.org/10.34944/dspace/2683
    Abstract
    This dissertation is focused on multiple testing procedures to be used in data that are naturally grouped or possess a spatial structure. We propose `Two-Stage' procedure to control the False Discovery Rate (FDR) in situations where one-sided hypothesis testing is appropriate, such as astronomical source detection. Similarly, we propose a `Three-Stage' procedure to control the mixed directional False Discovery Rate (mdFDR) in situations where two-sided hypothesis testing is appropriate, such as vegetation monitoring in remote sensing NDVI data. The Two and Three-Stage procedures have provable FDR/mdFDR control under certain dependence situations. We also present the Adaptive versions which are examined under simulation studies. The `Stages' refer to testing hypotheses both group-wise and individually, which is motivated by the belief that the dependencies among the p-values associated with the spatially oriented hypotheses occur more locally than globally. Thus, these `Staged' procedures test hypotheses in groups that incorporate the local, unknown dependencies of neighboring p-values. If a group is found significant, further investigation is done to the individual p-values within that group. For the vegetation monitoring data, we extend the investigation by providing some spatio-temporal models and forecasts to some regions where significant change was detected through the multiple testing procedure.
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