Least Squares Consistent Estimates for Arbitrary Regression Functions over an Abstract Space

In this article, we propose a new approach to sieve estimation for a general regression function when the dimension of the finite dimensional subspaces is a random quantity depending on the values of the observations. The technique is introduced with the help of a simulation study on a functional linear model under extremely mild assumptions. A sketch of the proof concerning the main statements is then given in the more general case when the regression function is not necessarily linear. Copyright © Taylor & Francis Group, LLC.