The application of Akaike information criterion based pruning to nonparametric density estimates

Abstract

This paper examines the application of Akaike (1974) information criterion (AIC) based pruning to the refinement of nonparametric density estimates obtained via the adaptive mixtures (AM) procedure of Priebe (see JASA, vol.89, no.427, p.796-806, 1994) and Marchette. The paper details a new technique that uses these two methods in conjunction with one another to predict the appropriate number of terms in the mixture model of an unknown density. Results that detail the procedure's performance when applied to different distributional classes are presented. Results are presented on artificially generated data, well known data sets, and some features for mammographic screening.