Defining a Correlation Measure for Random Variables Derived from SSP, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor) ; Aminzadeh-Gohari, Amin (Co-Supervisor)
Abstract
Studying the statistical dependence of two or several random variables is the basis of statistical estimation and prediction. The correlation measures such as mutual information, Pearson correlation, and maximal correlation are common tools in quantifying the extent to which two random variables are dependent. While such measures are highly informative and computationally simple for jointly Gaussian random variables, it is not the case for general random variables. Infinitely divisible random variables are typical examples that are characterized in the Fourier domain (characteristic functions are known); except for a few special cases, no closed-form expressions are available for the...
Cataloging briefDefining a Correlation Measure for Random Variables Derived from SSP, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor) ; Aminzadeh-Gohari, Amin (Co-Supervisor)
Abstract
Studying the statistical dependence of two or several random variables is the basis of statistical estimation and prediction. The correlation measures such as mutual information, Pearson correlation, and maximal correlation are common tools in quantifying the extent to which two random variables are dependent. While such measures are highly informative and computationally simple for jointly Gaussian random variables, it is not the case for general random variables. Infinitely divisible random variables are typical examples that are characterized in the Fourier domain (characteristic functions are known); except for a few special cases, no closed-form expressions are available for the...
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