Analysis of kernel density estimation of functions of random variables

DOI10.1080/10485250310001605441zbMath1054.62030OpenAlexW2039020723MaRDI QIDQ4470133

Ibrahim A. Ahmad, A. R. Mugdadi

Publication date: 22 June 2004

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485250310001605441

zbMATH Keywords

Bandwidth selectionDensity estimationAsymptotic expansionCentral limit theoremFunctions of random variablesKernel contrast

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05)

Related Items (3)

Estimation of convolution in the model with noise ⋮ A bandwidth selection for kernel density estimation of functions of random variables ⋮ Fast nonparametric estimation for convolutions of densities

Cites Work

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• Central limit theorem for integrated square error of multivariate nonparametric density estimators
• Conditional \(U\)-statistics
• Universally consistent conditional \(U\)-statistics
• A Brief Survey of Bandwidth Selection for Density Estimation
• Testing normality using kernel methods
• Root-nconvergent transformation-kernel density estimation
• Iterated Transformation-Kernel Density Estimation
• Consistent Model Specification Tests: Omitted Variables and Semiparametric Functional Forms
• A Class of Statistics with Asymptotically Normal Distribution
• Optimal bandwidths for kernel density estimators of functions of observations

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