Discrete mollification and automatic numerical differentiation

An automatic method for numerical differentiation, based on discrete mollification and the principle of generalized cross validation is presented. With data measured at a discrete set of points of a given interval, the method allows for the approximate recovery of the derivative function on the entire domain. No information about the noise is assumed. Error estimates are included together with several numerical examples of interest.