A cross-validation bandwidth choice for kernel density estimates with selection biased data

DOI10.1006/jmva.1997.1659zbMath0885.62050OpenAlexW2051939153MaRDI QIDQ1364666

Wu, Colin O.

Publication date: 23 April 1998

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

Full work available at URL: https://doi.org/10.1006/jmva.1997.1659

zbMATH Keywords

maximum likelihood estimationbandwidthcross-validationkernel density estimatemean integrated squared errorsbiased sampling model

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Minimax procedures in statistical decision theory (62C20)

Related Items (15)

An overview of nonparametric contributions to the problem of functional estimation from biased data ⋮ Transformation- based density estimation For weighted distributions ⋮ Nonparametric weighted estimators for biased data ⋮ Distribution estimation for biased data ⋮ Density estimation for biased data. ⋮ Binary surrogates with stratified samples when weights are unknown ⋮ Nonparametric density estimation in presence of bias and censoring ⋮ Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies ⋮ Generalized kernel regression estimator for dependent size-biased data ⋮ Density estimation under a two-sample semiparametric model ⋮ Nonparametric inference under competing risks and selection-biased sampling ⋮ On Wavelet Estimation of the Derivatives of a Density Based on Biased Data ⋮ Non-negative density estimation via wavelet block thresholding for biased data ⋮ Nonparametric regression estimators for length biased data ⋮ An improved minimum-distance texture estimator for speckled data under the \(\mathscr{G}^0\) model

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