Chehrazi, NaveedWeber, Thomas A.2013-07-122013-07-122013-07-12201010.1287/opre.1100.0814https://infoscience.epfl.ch/handle/20.500.14299/93282Many decision problems exhibit structural properties in the sense that the objective function is a composition of different component functions that can be identified using empirical data. We consider the approximation of such objective functions, subject to general monotonicity constraints on the component functions. Using a constrained B-spline approximation, we provide a data-driven robust optimization method for environments that can be sample-sparse. The method, which simultaneously identifies and solves the decision problem, is illustrated for the problem of optimal debt settlement in the credit-card industryB-splinesmonotone approximationnonparametric/semiparametric methodsrobust optimizationsample-sparse environmentstext::journal::journal article::research article