Global risk bounds and adaptation in univariate convex regression

We consider the problem of nonparametric estimation of a convex regression function $\phi_0$. We study the risk of the least squares estimator (LSE) under the natural squared error loss. We show that the risk is always bounded from above by $n^{-4/5}$ (upto logarithmic factors) while being much smaller when $\phi_0$ is well-approximable by a piecewise affine convex function with not too many affine pieces (in which case, the risk is atmost $1/n$ upto logarithmic factors). On the other hand, when $\phi_0$ has curvature, we show that no estimator can have risk smaller than a constant multiple of $n^{-4/5}$ in a very strong sense by proving a "local" minimax lower bound. We also study the case of model misspecification where we show that the LSE exhibits the same global behavior provided the loss is measured from the closest convex projection of the true regression function. In the process of deriving our risk bounds, we prove new results for the metric entropy of local neighborhoods of the space of univariate convex functions. These results, which may be of independent interest, demonstrate the non-uniform nature of the space of univariate convex functions in sharp contrast to classical function spaces based on smoothness constraints.