Abstract
We consider a least squares estimator for estimating a convex function f*: [0, 1]d → ℝ with bounded subgradients. A rate at which the sum of squared differences between the estimator and the true function f* converges to zero is computed. This work sheds light on computing the convergence rate of the multidimensional convex regression estimator.