Introduction to double robust methods for incomplete data
From MaRDI portal
Publication:1799345
DOI10.1214/18-STS647zbMath1397.62176WikidataQ54955574 ScholiaQ54955574MaRDI QIDQ1799345
Shaun R. Seaman, Stijn Vansteelandt
Publication date: 18 October 2018
Published in: Statistical Science (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ss/1525313141
imputationmissing dataempirical likelihoodsemiparametric methodsinverse probability weightingaugmented inverse probability weightingdoubly robustcalibration estimatorsdata-adaptive methods
Related Items (10)
Pattern graphs: a graphical approach to nonmonotone missing data ⋮ Causal inference with missingness in confounder ⋮ Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference ⋮ Treatment effect identification using two-level designs with partially ignorable missing data ⋮ An efficient doubly-robust imputation framework for longitudinal dropout, with an application to an Alzheimer's clinical trial ⋮ Kernel machines with missing covariates ⋮ Kernel machines with missing responses ⋮ Semiparametric Bayesian causal inference ⋮ Robust inference when combining inverse-probability weighting and multiple imputation to address missing data with application to an electronic health records-based study of bariatric surgery ⋮ Doubly robust difference-in-differences estimators
Cites Work
- Unnamed Item
- Unnamed Item
- Doubly Robust Estimation in Missing Data and Causal Inference Models
- Longitudinal data analysis using generalized linear models
- What is meant by ``missing at random?
- Demystifying double robustness: a comparison of alternative strategies for estimating a population mean from incomplete data
- Comment: Performance of double-robust estimators when ``inverse probability weights are highly variable
- Robust inference on average treatment effects with possibly more covariates than observations
- A semiparametric model selection criterion with applications to the marginal structural model
- The cluster bootstrap consistency in generalized estimating equations
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Semiparametric theory and missing data.
- Bounded, efficient and doubly robust estimation with inverse weighting
- Improved double-robust estimation in missing data and causal inference models
- Improved Doubly Robust Estimation When Data Are Monotonely Coarsened, with Application to Longitudinal Studies with Dropout
- Robust Estimation of Area Under ROC Curve Using Auxiliary Variables in the Presence of Missing Biomarker Values
- PERFORMANCE LIMITS FOR ESTIMATORS OF THE RISK OR DISTRIBUTION OF SHRINKAGE-TYPE ESTIMATORS, AND SOME GENERAL LOWER RISK-BOUND RESULTS
- Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data
- A Distributional Approach for Causal Inference Using Propensity Scores
- Confounder selection via penalized credible regions
- Some results on generalized difference estimation and generalized regression estimation for finite populations
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- The Generalized Estimating Equation Approach When Data Are Not Missing Completely at Random
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models
- Bias-Reduced Doubly Robust Estimation
- Twicing Kernels and a Small Bias Property of Semiparametric Estimators
- MODEL SELECTION AND INFERENCE: FACTS AND FICTION
- Weighted Estimators for Proportional Hazards Regression With Missing Covariates
- Doubly Robust Estimation of the Area Under the Receiver-Operating Characteristic Curve in the Presence of Verification Bias
- A Generalization of Sampling Without Replacement From a Finite Universe
This page was built for publication: Introduction to double robust methods for incomplete data