  In this paper we provide a computation algorithm to get a global solution for
the maximum rank correlation estimator using the mixed integer programming
(MIP) approach. We construct a new constrained optimization problem by
transforming all indicator functions into binary parameters to be estimated and
show that it is equivalent to the original problem. We also consider an
application of the best subset rank prediction and show that the original
optimization problem can be reformulated as MIP. We derive the non-asymptotic
bound for the tail probability of the predictive performance measure. We
investigate the performance of the MIP algorithm by an empirical example and
Monte Carlo simulations.
