Randomization test and correlation effects in high dimensional data

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dc.contributor.author Wang, Xiaofei
dc.date.accessioned 2012-07-17T20:35:11Z
dc.date.available 2012-07-17T20:35:11Z
dc.date.issued 2012-07-17
dc.identifier.uri http://hdl.handle.net/2097/14039
dc.description.abstract High-dimensional data (HDD) have been encountered in many fields and are characterized by a “large p, small n” paradigm that arises in genomic, lipidomic, and proteomic studies. This report used a simulation study that employed basic block diagonal covariance matrices to generate correlated HDD. Quantities of interests in such data are, among others, the number of ‘significant’ discoveries. This number can be highly variable when data are correlated. This project compared randomization tests versus usual t-tests for testing of significant effects across two treatment conditions. Of interest was whether the variance of the number of discoveries is better controlled in a randomization setting versus a t-test. The results showed that the randomization tests produced results similar to that of t-tests. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Randomization test en_US
dc.subject Correlation effect en_US
dc.subject High dimensional data en_US
dc.title Randomization test and correlation effects in high dimensional data en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor Gary Gadbury en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2012 en_US
dc.date.graduationmonth August en_US


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