Data-driven deconvolution

DOI10.1080/10485259908832766zbMath0936.62038OpenAlexW2082924331MaRDI QIDQ4265723

Christian H. Hesse

Publication date: 18 May 2000

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

Full work available at URL: https://doi.org/10.1080/10485259908832766

zbMATH Keywords

deconvolutionmean integrated squared errorasymptotic optimalityintegrated squared errorautomatic bandwidth selection

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Strong limit theorems (60F15)

Related Items (26)

Bootstrap bandwidth selection in kernel density estimation from a contaminated sample ⋮ The effects of error magnitude and bandwidth selection for deconvolution with unknown error distribution ⋮ Iterative density estimation from contaminated observations ⋮ Estimation of the mean residual life function in the presence of measurement errors ⋮ Deconvolution for an atomic distribution ⋮ On the performance of weighted bootstrapped kernel deconvolution density estimators ⋮ A ridge-parameter approach to deconvolution ⋮ Optimal bandwidth selection for multivariate kernel deconvolution density estimation ⋮ Estimation of distributions, moments and quantiles in deconvolution problems ⋮ Comment on identification and estimation of nonlinear models using two samples with nonclassical measurement errors ⋮ Practical bandwidth selection in deconvolution kernel density estimation ⋮ Optimal iterative density deconvolution ⋮ Deconvolution Estimation of Onset of Pregnancy with Replicate Observations ⋮ On the effect of misspecifying the error density in a deconvolution problem ⋮ On optimal estimation of the mode in nonparametric deconvolution problems ⋮ Data-driven deconvolution recursive kernel density estimators defined by stochastic approximation method ⋮ On optimal kernel choice for deconvolution ⋮ Intensity Estimation for Spatial Point Processes Observed with Noise ⋮ Bayesian Semiparametric Multivariate Density Deconvolution ⋮ Finite sample penalization in adaptive density deconvolution ⋮ Local bandwidth selectors for deconvolution kernel density estimation ⋮ A family of kernels and their associated deconvolving kernels for normally distributed measurement errors ⋮ Data-Driven Density Estimation in the Presence of Additive Noise with unknown Distribution ⋮ Support estimation via moment estimation in presence of noise ⋮ Penalized contrast estimator for adaptive density deconvolution ⋮ Least squares cross-validation for the kernel deconvolution density estimator

Cites Work

• Unnamed Item
• Correcting the negativity of high-order kernel density estimators
• On the optimal rates of convergence for nonparametric deconvolution problems
• Extent to which least-squares cross-validation minimises integrated square error in nonparametric density estimation
• An asymptotically optimal window selection rule for kernel density estimates
• MISE of kernel estimates of a density and its derivatives
• Rates of convergence of some estimators in a class of deconvolution problems
• A comparative study of several smoothing methods in density estimation
• Fourier methods for estimating mixing densities and distributions
• Deconvolving a density from partially contaminated observations
• Deconvolving a density from contaminated dependent observations
• A consistent nonparametric density estimator for the deconvolution problem
• Deconvolving kernel density estimators
• A data dependent approach to density estimation
• Optimal Rates of Convergence for Deconvolving a Density
• Choosing the window width when estimating a density

This page was built for publication: Data-driven deconvolution