Date of Graduation
8-2019
Document Type
Thesis
Degree Name
Master of Science in Statistics and Analytics (MS)
Degree Level
Graduate
Department
Graduate School
Advisor
Jyotishka Datta
Committee Member
John Tipton
Second Committee Member
Qingyang Zhang
Keywords
High-Dimensional Data, Multiple Testing, Statistics
Abstract
High dimensional data with sparsity is routinely observed in many scientific disciplines. Filtering out the signals embedded in noise is a canonical problem in such situations requiring multiple testing. The Benjamini--Hochberg procedure using False Discovery Rate control is the gold standard in large scale multiple testing. In Majumder et al. (2009) an internally cross-validated form of the procedure is used to avoid a costly replicate study and the complications that arise from population selection in such studies (i.e. extraneous variables). I implement this procedure and run extensive simulation studies under increasing levels of dependence among parameters and different data generating distributions and compare results with other common techniques. I illustrate that the internally cross-validated Benjamini--Hochberg procedure results in a significantly reduced false discovery rate, while maintaining a reasonable, though increased, false negative rate, and in a reduction to inherent variability under strong dependence structures when compared with the usual Benjamini--Hochberg procedure. In the discussion section, I describe some possibilities for relevant applications and future studies.
Citation
Price, J. D. (2019). Effect of Cross-Validation on the Output of Multiple Testing Procedures. Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3324