Density estimation for spherical data using nonparametric mixtures

Danli Xu and Yong Wang

Computational Statistics & Data Analysis, 2023, vol. 182, issue C

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.

Keywords: Spherical data; Density estimation; Mixture models; Nonparametric maximum likelihood; Bandwidth selection (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:182:y:2023:i:c:s0167947323000269

DOI: 10.1016/j.csda.2023.107715

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