Open AccessThis article is
- freely available
A Novel Nonparametric Distance Estimator for Densities with Error Bounds
Instituto de Engenharia Mecânica e Gestão Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
Computational Neuro Engineering Laboratory, University of Florida, EB451 Engineering Building, University of Florida, Gainesville, FL 32611, USA
* Author to whom correspondence should be addressed.
Received: 19 December 2012; in revised form: 25 April 2013 / Accepted: 28 April 2013 / Published: 6 May 2013
Abstract: The use of a metric to assess distance between probability densities is an important practical problem. In this work, a particular metric induced by an α-divergence is studied. The Hellinger metric can be interpreted as a particular case within the framework of generalized Tsallis divergences and entropies. The nonparametric Parzen’s density estimator emerges as a natural candidate to estimate the underlying probability density function, since it may account for data from different groups, or experiments with distinct instrumental precisions, i.e., non-independent and identically distributed (non-i.i.d.) data. However, the information theoretic derived metric of the nonparametric Parzen’s density estimator displays infinite variance, limiting the direct use of resampling estimators. Based on measure theory, we present a change of measure to build a finite variance density allowing the use of resampling estimators. In order to counteract the poor scaling with dimension, we propose a new nonparametric two-stage robust resampling estimator of Hellinger’s metric error bounds for heterocedastic data. The approach presents very promising results allowing the use of different covariances for different clusters with impact on the distance evaluation.
Keywords: generalized differential entropies; generalized differential divergences; Tsallis entropy; Hellinger metric; nonparametric estimators; heterocedastic data
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
Carvalho, A.R.; Tavares, J.M.R.S.; Principe, J.C. A Novel Nonparametric Distance Estimator for Densities with Error Bounds. Entropy 2013, 15, 1609-1623.
Carvalho AR, Tavares JMRS, Principe JC. A Novel Nonparametric Distance Estimator for Densities with Error Bounds. Entropy. 2013; 15(5):1609-1623.
Carvalho, Alexandre R.; Tavares, João M.R.S.; Principe, Jose C. 2013. "A Novel Nonparametric Distance Estimator for Densities with Error Bounds." Entropy 15, no. 5: 1609-1623.