Integrated squared error estimation of normal mixtures

Based on the empirical characteristic function, the integrated squared error criterion for normal mixtures is shown to have a simple form for a particular weight function. When the parameter of that function is chosen as the smoothed cross-validation selector in kernel density estimation, the estimator which minimises the criterion is shown to perform well in a simulation study. In comparison with maximum likelihood and a new recently proposed method there are better bias and standard deviation results for the method of this paper. Furthermore, the new estimator is less likely to fail and is appreciably more robust. (C) 2002 Elsevier B.V. All rights reserved.