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

fYear

1994

fDate

27-29 Oct 1994

Firstpage

74

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on

Conference_Location

Alexandria, VA

Print_ISBN

0-7803-2761-6

Type

conf

DOI

10.1109/WITS.1994.513903

Filename

513903