  Based on the notion of maximal correlation, Kimeldorf, May and Sampson (1980)
introduce a measure of correlation between two random variables, called the
"concordant monotone correlation" (CMC). We revisit, generalize and prove new
properties of this measure of correlation. It is shown that CMC captures
various types of correlation detected in measures of rank correlation like the
Kendall tau correlation. We show that the CMC satisfies the data processing and
tensorization properties (that make ordinary maximal correlation applicable to
problems in information theory). Furthermore, CMC is shown to be intimately
related to the FKG inequality. Furthermore, a combinatorical application of CMC
is given for which we do not know of another method to derive its result.
Finally, we study the problem of the complexity of the computation of the CMC,
which is a non-convex optimization problem with local maximas. We give a simple
but exponential-time algorithm that is guaranteed to output the exact value of
the generalized CMC.
