Karl Pearson developed the correlation coefficient r(X,Y) in 1890s vastly underestimates dependence between two series. Vinod(2014} develops new generalized correlation coefficients so that when r*(Y|X) r*(X|Y) then X is the "kernel cause'' of Y. Vinod (2015) reports simulations favoring kernel...

Usual correlations assume linearity. If new generalized correlations satisfy r*(Y |X) r*(X|Y ), X better predicts Y than vice versa. Then we say that X "causes" Y . Thus, Vinod (2013) revives Granger's instantaneous causality concept. Mooij et al. (2014) and their references seem unaware of...

Karl Pearson developed the correlation coefficient r(X,Y) in 1890's. Vinod (2014) develops new generalized correlation coefficients so that when r*(Y|X) r*(X|Y) then X is the "kernel cause" of Y. Vinod (2015a) argues that kernel causality amounts to model selection between two kernel...

A popular F test of Granger-causality relies on normally distributed errors of ordinary least squares (OLS) linear regressions. There is a long-standing need for a user-friendly algorithm replacing the OLS by kernel regressions, and the F test by a bootstrap. This paper introduces a version...