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Guus Berkelmans and Joris Pries (both CWI) - A general measure of dependency between random variables Supervisor: Rob van der Mei and Sandjai Bhulai Recorded full presentation Abstract Measuring and quantifying dependencies between random variables (RV's) can give critical insights into a data-set. Typical questions are: `Do underlying relationships exist?', `Are some variables redundant?' and `Is some target variable Y highly or weakly dependent on variable X?' Interestingly, despite the evident need for a general-purpose measure of dependency between RV's, common practice of data analysis is that most data analysts use the classical correlation coefficient (CC) to quantify dependence between RV's, while it is well-recognized that the CC is essentially a measure for linear dependency only. Although many attempts have been made to define more generic dependency measures, there is yet no general consensus on a standard, general-purpose dependency function. In fact, several ideal properties of a dependency function have been proposed, but without much argumentation. Motivated by this, we discuss and revise the list of desired properties and propose a new dependency function that meets all these requirements. This general-purpose dependency function provides data analysts a powerful means to quantify the level of dependence between variables. |
