Date of Graduation
Master of Science in Statistics and Analytics (MS)
Statistics and Analytics
Mark E. Arnold
Second Committee Member
Biweight midcorrelation, Copula correlation, Distance correlation, Maximal information coefficient correlation, Mutual information, Spearman’s correlation
Measuring and testing dependence between random variables is of great importance in many scientific fields. In the case of linearly correlated variables, Pearson’s correlation coefficient is a commonly used measure of the correlation strength. In the case of nonlinear correlation, several innovative measures have been proposed, such as distance-based correlation, rank-based correlations, and information theory-based correlation. This thesis focuses on the statistical comparison of several important correlations, including Spearman’s correlation, mutual information, maximal information coefficient, biweight midcorrelation, distance correlation, and copula correlation, under various simulation settings such as correlative patterns and the level of random noise. Furthermore, we apply those correlations with the overall best performance to a real genomic data set, to study the co-expression between genes in serous ovarian cancer.
Ibrahim, E. A. (2020). Comparative Evaluation of Statistical Dependence Measures. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3903