Gaussian kernels for density estimation with compositional data
Common simplifications of the bandwidth matrix cannot be applied to existing kernels for density estimation with compositional data. In this paper, kernel density estimation methods are modified on the basis of recent developments in compositional data analysis and bandwidth matrix selection theory....
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| Veröffentlicht in: | Computers & geosciences 2011-05, Vol.37 (5), p.702-711 |
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| container_title | Computers & geosciences |
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| creator | Chacón, J.E. Mateu-Figueras, G. Martín-Fernández, J.A. |
| description | Common simplifications of the bandwidth matrix cannot be applied to existing kernels for density estimation with compositional data. In this paper, kernel density estimation methods are modified on the basis of recent developments in compositional data analysis and bandwidth matrix selection theory. The isometric log-ratio normal kernel is used to define a new estimator in which the smoothing parameter is chosen from the most general class of bandwidth matrices on the basis of a recently proposed plug-in algorithm. Both simulated and real examples are presented in which the behaviour of our approach is illustrated, which shows the advantage of the new estimator over existing proposed methods. |
| doi_str_mv | 10.1016/j.cageo.2009.12.011 |
| format | Article |
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| issn | 0098-3004 1873-7803 |
| language | eng |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | algorithms Bandwidth Computer simulation computers data analysis data collection Density Estimators Gaussian Isometric log-ratio Kernels Mathematical analysis Normal distribution Simplex Simplification |
| title | Gaussian kernels for density estimation with compositional data |
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