On classification with nonignorable missing data

DOI10.1016/J.JMVA.2021.104755zbMath1473.62225OpenAlexW3138067111MaRDI QIDQ2034467

Majid Mojirsheibani

Publication date: 22 June 2021

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2021.104755

zbMATH Keywords

classificationconvergencekernelregressionmissing data

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)

Related Items (1)

On the maximal deviation of kernel regression estimators with NMAR response variables

Cites Work

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• Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas
• D-vine copula based quantile regression
• Kernel regression estimation for incomplete data with applications
• On the \(L_p\) norms of kernel regression estimators for incomplete data with applications to classification
• Fitting time series models for longitudinal surveys with nonignorable missing data
• An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
• A note on the equivalence of two semiparametric estimation methods for nonignorable nonresponse
• The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments
• Identification problem of transition models for repeated measurement data with nonignorable missing values
• Bandwidth choice for nonparametric classification
• Empirical likelihood inference for mean functionals with nonignorably missing response data
• A distribution-free theory of nonparametric regression
• Weak convergence and empirical processes. With applications to statistics
• Semiparametric inverse propensity weighting for nonignorable missing data
• Semiparametric Estimating Equations Inference with Nonignorable Nonresponse
• Empirical likelihood and Wilks phenomenon for data with nonignorable missing values
• Likelihood adjusted for nonignorable missing covariate values with unspecified propensity in generalized linear models
• Semiparametric maximum likelihood estimation with data missing not at random
• Pseudo likelihood‐based estimation and testing of missingness mechanism function in nonignorable missing data problems
• Sequentially additive nonignorable missing data modelling using auxiliary marginal information
• A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data
• Copula-Based Regression Estimation and Inference
• Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data
• Empirical likelihood for estimating equations with nonignorably missing data
• Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

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