## Measuring Dependence via Mutual Information

 dc.contributor.author Lu, Shan en dc.contributor.department Mathematics and Statistics en dc.contributor.supervisor Alajaji, Fady en dc.contributor.supervisor Takahara, Glen en dc.date 2011-09-30 14:29:35.153 dc.date.accessioned 2011-10-03T18:19:05Z dc.date.available 2011-10-03T18:19:05Z dc.date.issued 2011-10-03 dc.degree.grantor Queen's University at Kingston en dc.description Thesis (Master, Mathematics & Statistics) -- Queen's University, 2011-09-30 14:29:35.153 en dc.description.abstract Considerable research has been done on measuring dependence between random variables. The correlation coefficient is the most widely studied linear measure of dependence. However, the limitation of linearity limits its application. The informational coefficient of correlation is defined in terms of mutual information. It also has some deficiencies, such as it is only normalized to continuous random variables. Based on the concept of the informational coefficient of correlation, a new dependence measure, which we call the L-measure, is proposed in this work which generalizes Linfoot's measure for both continuous and discrete random variables. To further elucidate its properties, simulated models are used, and estimation algorithms are also discussed. Furthermore, another measure based on the L-measure, which we call the intrinsic L-measure, is studied for the purpose of studying nonlinear dependence. Based on criteria for a dependence measure presented by Renyi and simulation results in this thesis, we believe that the L-measure is satisfactory as a dependence measure. en dc.description.degree M.Sc. en dc.identifier.uri http://hdl.handle.net/1974/6816 dc.language.iso eng en dc.relation.ispartofseries Canadian theses en dc.rights This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. en dc.subject Mutual Information en dc.subject Dependence Measure en dc.title Measuring Dependence via Mutual Information en dc.type thesis en
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