Inference of nonparametric hypothesis testing on high dimensional longitudinal data and its application in DNA copy number variation and micro array data analysis

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dc.contributor.author Zhang, Ke
dc.date.accessioned 2008-12-19T21:21:44Z
dc.date.available 2008-12-19T21:21:44Z
dc.date.issued 2008-12-19T21:21:44Z
dc.identifier.uri http://hdl.handle.net/2097/1105
dc.description.abstract High throughput screening technologies have generated a huge amount of biological data in the last ten years. With the easy availability of array technology, researchers started to investigate biological mechanisms using experiments with more sophisticated designs that pose novel challenges to statistical analysis. We provide theory for robust statistical tests in three flexible models. In the first model, we consider the hypothesis testing problems when there are a large number of variables observed repeatedly over time. A potential application is in tumor genomics where an array comparative genome hybridization (aCGH) study will be used to detect progressive DNA copy number changes in tumor development. In the second model, we consider hypothesis testing theory in a longitudinal microarray study when there are multiple treatments or experimental conditions. The tests developed can be used to detect treatment effects for a large group of genes and discover genes that respond to treatment over time. In the third model, we address a hypothesis testing problem that could arise when array data from different sources are to be integrated. We perform statistical tests by assuming a nested design. In all models, robust test statistics were constructed based on moment methods allowing unbalanced design and arbitrary heteroscedasticity. The limiting distributions were derived under the nonclassical setting when the number of probes is large. The test statistics are not targeted at a single probe. Instead, we are interested in testing for a selected set of probes simultaneously. Simulation studies were carried out to compare the proposed methods with some traditional tests using linear mixed-effects models and generalized estimating equations. Interesting results obtained with the proposed theory in two cancer genomic studies suggest that the new methods are promising for a wide range of biological applications with longitudinal arrays. en
dc.language.iso en_US en
dc.publisher Kansas State University en
dc.subject high dimensional data en
dc.subject longitudinal analysis en
dc.subject nonparametric inference en
dc.subject hypothesis testing en
dc.subject DNA copy number variation en
dc.title Inference of nonparametric hypothesis testing on high dimensional longitudinal data and its application in DNA copy number variation and micro array data analysis en
dc.type Dissertation en
dc.description.degree Doctor of Philosophy en
dc.description.level Doctoral en
dc.description.department Department of Statistics en
dc.description.advisor Haiyan Wang en
dc.subject.umi Biology, Biostatistics (0308) en
dc.subject.umi Statistics (0463) en
dc.date.published 2008 en
dc.date.graduationmonth December en


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