Empirical likelihood confidence bands for distribution functions with missing responses

DOI10.1016/J.JSPI.2010.03.044zbMath1188.62148OpenAlexW2035000108MaRDI QIDQ974524

Qi Hua Wang, Yong Song Qin

Publication date: 3 June 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2010.03.044

zbMATH Keywords

imputationmissing at randomempirical likelihoodconfidence bandsinverse probability weighting

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05) Nonparametric tolerance and confidence regions (62G15)

Related Items (13)

Statistical inference for nonignorable missing-data problems: a selective review ⋮ Local likelihood of quantile difference under left-truncated, right-censored and dependent assumptions ⋮ A simple approach to construct confidence bands for a regression function with incomplete data ⋮ Efficient estimation of quantiles in missing data models ⋮ Nonlinear wavelet density estimation with data missing at random when covariates are present ⋮ On density and regression estimation with incomplete data ⋮ Convergence rate of wavelet density estimator with data missing randomly when covariables are present ⋮ Kernel regression estimation for incomplete data with applications ⋮ Empirical likelihood-based inferences in varying coefficient models with missing data ⋮ Empirical likelihood of quantile difference with missing response when high-dimensional covariates are present ⋮ Robust location estimators in regression models with covariates and responses missing at random ⋮ A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables ⋮ Robust estimation of distribution functions and quantiles with non-ignorable missing data

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