Doubly robust empirical likelihood inference in covariate-missing data problems
DOI10.1080/02331888.2015.1135925zbMath1357.62059OpenAlexW2462877998MaRDI QIDQ2953454
Publication date: 4 January 2017
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888.2015.1135925
efficiencyinfluence functionmissing at randomempirical likelihoodinverse probability weightingpropensity scoreunbiased estimating functiondouble robustpseudo likelihoodaugmented inverse probability weightingHorvitz and Thompson estimatorcovariate-missing
Nonparametric robustness (62G35) Nonparametric estimation (62G05) Censored data models (62N01) Sampling theory, sample surveys (62D05) General nonlinear regression (62J02)
Cites Work
- Empirical likelihood-based inference under imputation for missing response data
- Semiparametric theory and missing data.
- Bounded, efficient and doubly robust estimation with inverse weighting
- Empirical Likelihood in Missing Data Problems
- Semiparametric model-based inference in the presence of missing responses
- A Distributional Approach for Causal Inference Using Propensity Scores
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- Empirical likelihood ratio confidence intervals for a single functional
- Inference and missing data
- Regression Analysis with Missing Covariate Data Using Estimating Equations
- Empirical likelihood-based inference in linear errors-in-covariables models with validation data
- Information Recovery in a Study With Surrogate Endpoints
- Empirical Likelihood Inference in Nonlinear Errors-in-Covariables Models With Validation Data
- Missing-Data Methods for Generalized Linear Models
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