Critical Values of a Kernel Density-based Mutual Information Estimator
R. May, Graeme C. Dandy, Holger Robert Maier, T.M.K.G. Fernando · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Recently, mutual information (MI) has become widely recognized as a statistical measure of dependence that is suitable for applications where data are non-Gaussian, or where the dependency between variables is non-linear. However, a significant disadvantage of this measure is the inability to define an analytical expression for the distribution of MI estimators, which are based upon a finite dataset. This paper deals specifically with a popular kernel density based estimator, for which the distribution is determined empirically using Monte Carlo simulation. The application of the critical values of MI derived from this distribution to a test for independence is demonstrated within the context of a benchmark input variable selection problem.