Estimation of densities and derivatives of densities with directional data.

DOI10.1006/jmva.1999.1861zbMath1054.62033OpenAlexW2022708133MaRDI QIDQ1570287

Jussi Klemelä

Publication date: 2000

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1006/jmva.1999.1861

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Density estimation (62G07)

Related Items (24)

Adaptive Density Estimation on the Circle by Nearly Tight Frames ⋮ Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection ⋮ Nonparametric multiplicative bias correction for von Mises kernel circular density estimator ⋮ Nonparametric Rotations for Sphere-Sphere Regression ⋮ Recent advances in directional statistics ⋮ A plug-in rule for bandwidth selection in circular density estimation ⋮ Locally adaptive density estimation on the unit sphere using needlets ⋮ Kernel density estimation on the torus ⋮ Validating protein structure using kernel density estimates ⋮ Kernel density classification for spherical data ⋮ Local binary regression with spherical predictors ⋮ A Bayesian procedure for bandwidth selection in circular kernel density estimation ⋮ Quantitative central limit theorems for Mexican needlet coefficients on circular Poisson fields ⋮ ANALYZING THE RANDOM COEFFICIENT MODEL NONPARAMETRICALLY ⋮ Nonparametric estimation of directional highest density regions ⋮ Exact risk improvement of bandwidth selectors for kernel density estimation with directional data ⋮ Adaptive optimal kernel density estimation for directional data ⋮ On a Mixture Model for Directional Data on the Sphere ⋮ Local polynomial regression for circular predictors ⋮ Automatic bandwidth selection for circular density estimation ⋮ Robust comparison of kernel densities on spherical domains ⋮ Adaptive density estimation for directional data using needlets ⋮ Unnamed Item ⋮ Lower bounds for the asymptotic minimax risk with spherical data

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• Kernel density estimation revisited
• On Choosing a Delta-Sequence
• On the Estimation of the Probability Density, I

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