A constrained regression technique for cocomo calibration

Building cost estimation models is often considered a search problem in which the solver should return an optimal solution satisfying an objective function. This solution also needs to meet certain constraints. For example, a solution for the estimates coefficients of COCOMO models must be non-negative. In this research, we introduce a constrained regression technique that uses objective functions and constraints to estimate the coefficients of the COCOMO models. To access the performance of the proposed technique, we run a cross-validation procedure and compare the prediction accuracy from different approaches such as least squares, stepwise, Lasso, and Ridge regression. Our result suggests that the regression model that minimizes the sum of relative errors and imposes non-negative coefficients is a favorable technique for calibrating the COCOMO model parameters.