MISE of kernel estimates of a density and its derivatives

DOI10.1016/0167-7152(87)90072-1zbMath0635.62028OpenAlexW1964014197MaRDI QIDQ1097603

Radhey S. Singh

Publication date: 1987

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(87)90072-1

zbMATH Keywords

upper boundkernel estimatorsrates of convergencedensity estimationmean integrated square errordensity derivativesexact asymptotic value

Mathematics Subject Classification ID

Nonparametric estimation (62G05)

Related Items (10)

Data-driven deconvolution ⋮ Computationally efficient classes of higher‐order kernel functions ⋮ Modal clustering asymptotics with applications to bandwidth selection ⋮ On kernel density derivative estimation ⋮ Direct Density Derivative Estimation ⋮ Differences and derivatives in kernel estimation ⋮ A NONPARAMETRIC HELLINGER METRIC TEST FOR CONDITIONAL INDEPENDENCE ⋮ Feature significance for multivariate kernel density estimation ⋮ Sequential estimation of a density and its derivatives with bounded MISE. ⋮ Statistical modeling of directional data using a robust hierarchical von Mises distribution model: perspectives for wind energy

Cites Work

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• On the exact asymptotic behavior of estimators of a density and its derivatives
• Improvement on some known nonparametric uniformly consistent estimators of derivatives of a density
• Remarks on Some Nonparametric Estimates of a Density Function
• On the Integral Mean Square Error of Some Nonparametric Estimates for the Density Function
• Estimation of Probability Density by an Orthogonal Series
• Estimation of a Probability Density Function and Its Derivatives
• Curve Estimates
• On the Estimation of the Probability Density, I
• On Estimation of a Probability Density Function and Mode

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