Density estimation for spherical data using nonparametric mixtures
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Publication:6113819
DOI10.1016/j.csda.2023.107715OpenAlexW4319870166MaRDI QIDQ6113819
Publication date: 11 July 2023
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2023.107715
Cites Work
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- Kernel density estimation with spherical data
- Exact risk improvement of bandwidth selectors for kernel density estimation with directional data
- The geometry of mixture likelihoods, part II: The exponential family
- The geometry of mixture likelihoods: A general theory
- Unifying the derivations for the Akaike and corrected Akaike information criteria.
- A combined adaptive-mixtures/plug-in estimator of multivariate probability densities
- Some properties of a Cauchy family on the sphere derived from the Möbius transformations
- Model-based clustering on the unit sphere with an illustration using gene expression profiles
- Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
- On Optimal Tests for Rotational Symmetry Against New Classes of Hyperspherical Distributions
- Density estimation using non-parametric and semi-parametric mixtures
- On Fast Computation of the Non-Parametric Maximum Likelihood Estimate of a Mixing Distribution
- Nonparametric multivariate density estimation using mixtures
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