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Entropy 2014, 16(2), 921-942; doi:10.3390/e16020921

Statistical Analysis of Distance Estimators with Density Differences and Density Ratios

Nagoya University, Furocho, Chikusaku, Nagoya 464-8603, Japan
Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan
Author to whom correspondence should be addressed.
Received: 21 October 2013 / Revised: 27 January 2014 / Accepted: 7 February 2014 / Published: 17 February 2014
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Estimating a discrepancy between two probability distributions from samples is an important task in statistics and machine learning. There are mainly two classes of discrepancy measures: distance measures based on the density difference, such as the Lp-distances, and divergence measures based on the density ratio, such as the Φ-divergences. The intersection of these two classes is the L1-distance measure, and thus, it can be estimated either based on the density difference or the density ratio. In this paper, we first show that the Bregman scores, which are widely employed for the estimation of probability densities in statistical data analysis, allows us to estimate the density difference and the density ratio directly without separately estimating each probability distribution. We then theoretically elucidate the robustness of these estimators and present numerical experiments. View Full-Text
Keywords: density difference; density ratio; L1-distance; Bregman score; robustness density difference; density ratio; L1-distance; Bregman score; robustness

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Kanamori, T.; Sugiyama, M. Statistical Analysis of Distance Estimators with Density Differences and Density Ratios. Entropy 2014, 16, 921-942.

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