Optimal smoothing parameters for multivariate fized and adaptive kernel methods

Abstract

Except in special cases optimum smoothing parameters of kernel methods are difficult to obtain for small samples, and large sample results are often used. Simulation is used to obtain finite sample optimum smoothing parameters and mean integrated square errors for the bivariate normal density. For this example, comparison is made of finite and asymptotic results, and of fixed and adaptive kernel methods. Further comparisons are made of fixed and adaptive methods by considering four other different types of density. Finally, some examples are given.