A Non-Asymptotic Approach to Local Modelling

Local models and methods construct function estimates or predictions from observations in a local neighborhood of the point of interest. The bandwidth, i.e., how large the local neighborhood should be, is often determined based on asymptotic analysis. In this paper, an alternative, non-asymptotic approach that minimizes a uniform upper bound on the mean square error for a linear estimate is proposed. It is shown, for the scalar case, that the solution is obtained from a quadratic program, and that it maintains many of the key features of the asymptotic approaches. Moreover, examples show that the proposed approach in some cases is superior to an asymptotically based local linear estimator.