Least squares fit of definite quadratic forms by convex programming

This paper considers the problem of fitting a quadratic regression law subject to the condition that the fitted surface be convex (concave). A computational algorithm is described for determining the constrained regression coefficients using an existing non-linear programming algorithm. Some of the properties of the estimators are described.