Sensitivity to Distributional Assumptions in Estimation of the ODP Thresholding Function

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.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Bunn, Wendy Jill

• estimation
• gene expression
• multiple hypothesis testing
• multiple comparisons
• nonnormal
• optimal discovery procedure
• statistics
• Statistics and Probability

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/953

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1952