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Abstract
A class of linear programming problems is analyzed here, where some of the parameters are estimated by methods like least squares and are therefore stochastic. Since the decision vector may also be stochastic in this framework, this leads to bilinear problems in stochastic programming in suitable cases. Further, the conventional methods for applying programming models using regression estimates for its parameters appear to raise questions of validation of such models. Some aspects of the validation problems involving the risk aversion parameter are also analyzed here.