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Full-Text Articles in Physical Sciences and Mathematics
Beta Mixture And Contaminated Model With Constraints And Application With Micro-Array Data, Ya Qi
Beta Mixture And Contaminated Model With Constraints And Application With Micro-Array Data, Ya Qi
Theses and Dissertations--Statistics
This dissertation research is concentrated on the Contaminated Beta(CB) model and its application in micro-array data analysis. Modified Likelihood Ratio Test (MLRT) introduced by [Chen et al., 2001] is used for testing the omnibus null hypothesis of no contamination of Beta(1,1)([Dai and Charnigo, 2008]). We design constraints for two-component CB model, which put the mode toward the left end of the distribution to reflect the abundance of small p-values of micro-array data, to increase the test power. A three-component CB model might be useful when distinguishing high differentially expressed genes and moderate differentially expressed genes. If the null hypothesis above …
Serial Testing For Detection Of Multilocus Genetic Interactions, Zaid T. Al-Khaledi
Serial Testing For Detection Of Multilocus Genetic Interactions, Zaid T. Al-Khaledi
Theses and Dissertations--Statistics
A method to detect relationships between disease susceptibility and multilocus genetic interactions is the Multifactor-Dimensionality Reduction (MDR) technique pioneered by Ritchie et al. (2001). Since its introduction, many extensions have been pursued to deal with non-binary outcomes and/or account for multiple interactions simultaneously. Studying the effects of multilocus genetic interactions on continuous traits (blood pressure, weight, etc.) is one case that MDR does not handle. Culverhouse et al. (2004) and Gui et al. (2013) proposed two different methods to analyze such a case. In their research, Gui et al. (2013) introduced the Quantitative Multifactor-Dimensionality Reduction (QMDR) that uses the overall …
Inference Using Bhattacharyya Distance To Model Interaction Effects When The Number Of Predictors Far Exceeds The Sample Size, Sarah A. Janse
Inference Using Bhattacharyya Distance To Model Interaction Effects When The Number Of Predictors Far Exceeds The Sample Size, Sarah A. Janse
Theses and Dissertations--Statistics
In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that …