Estimation of mixture densities from histograms [signal classification]
Many signals and statistical distributions are a mixture of component signals or distributions. Current methods for estimating the proportion of each component assume a parametric form for the components. We introduce nonparametric methods, based on projections onto convex sets, to address the many...
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| creator | Rouse, D.M. Trussell, H.J. |
| description | Many signals and statistical distributions are a mixture of component signals or distributions. Current methods for estimating the proportion of each component assume a parametric form for the components. We introduce nonparametric methods, based on projections onto convex sets, to address the many practical cases where parametric models are not applicable. Comparisons are made with parametric methods and discussed for special cases where both methods can be used. |
| doi_str_mv | 10.1109/ICASSP.2004.1326316 |
| format | Conference Proceeding |
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| identifier | ISSN: 1520-6149 |
| ispartof | 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.2, p.ii-549 |
| issn | 1520-6149 2379-190X |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Histograms Information, signal and communications theory Least squares approximation Maximum likelihood estimation Parametric statistics Pixel Quality of service Remote sensing Signal and communications theory Signal, noise Spatial resolution Statistical distributions Telecommunication traffic Telecommunications and information theory |
| title | Estimation of mixture densities from histograms [signal classification] |
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