Recent technological advances in fields like medicine and genomics have produced high-dimensional data sets and a challenge to correctly interpret experimental results. The Optimal Discovery Procedure (ODP) (Storey 2005) builds on the framework of Neyman-Pearson hypothesis testing to optimally test thousands of hypotheses simultaneously. The method relies on the assumption of normally distributed data; however, many applications of this method will violate this assumption. This thesis investigates the sensitivity of this method to detection of significant but nonnormal data. Overall, estimation of the ODP with the method described in this thesis is satisfactory, except when the nonnormal alternative distribution has high variance and expectation only one standard deviation away from the null distribution.
College and Department
Physical and Mathematical Sciences; Statistics
BYU ScholarsArchive Citation
Bunn, Wendy Jill, "Sensitivity to Distributional Assumptions in Estimation of the ODP Thresholding Function" (2007). All Theses and Dissertations. 953.
estimation, gene expression, multiple hypothesis testing, multiple comparisons, nonnormal, optimal discovery procedure, statistics