This paper supplements the results of a new statistical approach to the problem of incomplete information in stochastic programming. The tools of nondifferentiable optimization used here, help to prove the consistency and asymptotic normality of (approximate) optimal solutions without unnatural smoothness assumptions. This allows the theory to take into account the presence of constraints
