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A Visualization Technique for Studying the Iterative Estimation of Mixture Densities

Jeffrey L. Solka, Wendy L. Poston and Edward J. Wegman
Journal of Computational and Graphical Statistics
Vol. 4, No. 3 (Sep., 1995), pp. 180-198
DOI: 10.2307/1390846
Stable URL: http://www.jstor.org/stable/1390846
Page Count: 19
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A Visualization Technique for Studying the Iterative Estimation of Mixture Densities
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Abstract

This article focuses on recent work that analyzes the expectation maximization (EM) evolution of mixtures-based estimators. The goal of this research is the development of effective visualization techniques to portray the mixture model parameters as they change in time. This is an inherently high-dimensional process. Techniques are presented that portray the time evolution of univariate, bivariate, and trivariate finite and adaptive mixtures estimators. Adaptive mixtures is a recently developed variable bandwidth kernel estimator where each of the kernels is not constrained to reside at a sample location. The future role of these techniques in developing new versions of the adaptive mixtures procedure is also discussed.

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