k-Nearest neighbor density estimation on Riemannian Manifolds
classification
🧮 math.ST
stat.TH
keywords
manifoldsasymptoticconsiderestimatork-nearestneighborriemanniananalyzed
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In this paper, we consider a k-nearest neighbor kernel type estimator when the random variables belong in a Riemannian manifolds. We study asymptotic properties such as the consistency and the asymptotic distribution. A simulation study is also consider to evaluate the performance of the proposal. Finally, to illustrate the potential applications of the proposed estimator, we analyzed two real example where two different manifolds are considered.
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Cited by 1 Pith paper
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Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation
PMM-MASEM introduces a gated PMM2/PMM3 density estimator on kNN shell spacings for MASEM, reducing MSE by 22-36% on asymmetric regimes while falling back to MLE on flat Exp(1) spacings and showing mixed results overall.
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