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Multivariate Parametric Density Estimation Based On The Modified Cramér-von Mises Distance

Krauthausen, Peter; Eberhardt, Henning; Hanebeck, Uwe D.

In this paper, a novel distance-based density estimation method is proposed, which considers the overall density function in the goodness-of-fit. In detail, the parameters of Gaussian mixture densities are estimated from samples, based on the distance of the cumulative distributions over the entire state space. Due to the ambiguous definition of the standard multivariate cumulative distribution, the Localized Cumulative Distribution and a modified Cram\'{e}r-von Mises distance measure are employed. A further contribution is the derivation of a simple-to-implement optimization procedure for the optimization problem. The proposed approach's good performance in estimating arbitrary Gaussian mixture densities is shown in an experimental comparison to the Expectation Maximization algorithm for Gaussian mixture densities.

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Volltext §
DOI: 10.5445/IR/1000035045
DOI: 10.1109/MFI.2010.5604448
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4244-5424-2
KITopen-ID: 1000035045
Erschienen in Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, Sept. 5-7, 2010
Verlag IEEE, Piscataway
Seiten 199-204
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