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- W4319870166 abstract "Nonparametric density estimation is studied for spherical data that may arise in many scientific and practical fields. In particular, nonparametric mixture models based on likelihood maximization are used. A nonparametric mixture has component distributions mixed together with a mixing distribution that is completely unspecified and needs to be determined from data. For mixture components, a two-parameter distribution family can be used, with one parameter as the mixing variable and the other to control the smoothness of the density estimator. For example, the popular von Mises-Fisher distributions can be readily used for this purpose. Numerical studies with various spherical data sets show that the resultant mixture-based density estimators are strong competitors with the best of the other density estimators." @default.
- W4319870166 created "2023-02-11" @default.
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- W4319870166 date "2023-06-01" @default.
- W4319870166 modified "2023-10-16" @default.
- W4319870166 title "Density estimation for spherical data using nonparametric mixtures" @default.
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- W4319870166 doi "https://doi.org/10.1016/j.csda.2023.107715" @default.
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