Title :
The application of Akaike information criterion based pruning to nonparametric density estimates
Author :
Solka, Jeff ; Priebe, Carey ; Rogers, George ; Poston, Wendy ; Marchette, David
Author_Institution :
Naval Surface Warfare Center, Dahlgren, VA, USA
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
Keywords :
estimation theory; information theory; nonparametric statistics; Akaike information criterion; adaptive mixtures; artificially generated data; data sets; distributional classes; mammographic screening; mixture model; nonparametric density estimates; performance; pruning; Gaussian distribution; Kernel; Maximum likelihood estimation; Petroleum; Predictive models;
Conference_Titel :
Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
Conference_Location :
Alexandria, VA
Print_ISBN :
0-7803-2761-6
DOI :
10.1109/WITS.1994.513903
