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

Proceedings of 1994 Workshop on Information Theory and Statistics Pub Date : 1994-10-27 DOI:10.1109/WITS.1994.513903

J. Solka, C. Priebe, G. Rogers, W. Poston, D. Marchette

引用次数: 1

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.

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基于Akaike信息准则的剪枝在非参数密度估计中的应用

本文研究了Akaike(1974)基于信息准则(AIC)的剪枝在Priebe自适应混合(AM)过程获得的非参数密度估计的细化中的应用(见JASA, vol.89, no. 11)。(4)， p.796-806, 1994)。本文详细介绍了一种新技术，该技术将这两种方法相互结合，以预测未知密度混合模型中的适当项数。给出了应用于不同分布类时该过程性能的详细结果。结果呈现在人工生成的数据，众所周知的数据集，以及乳房x线摄影筛查的一些特征。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of 1994 Workshop on Information Theory and Statistics

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