Asymptotics for $M$-Estimators Defined by Convex Minimization
由凸最小化定义的 M 估计量的渐近理论
We consider $M$-estimators defined by minimization of a convex criterion function, not necessarily smooth. Our asymptotic results generalize some of those concerning the LAD estimators. We establish a Bahadur-type strong approximation and bounds on the rate of convergence.