Empirical likelihood-based inference under imputation for missing response data

By Qihua Wang, J. Rao. 2002

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a0a4a6f35d4edd8c024330afa4a9e759fc4e8461

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Empirical Likelihood Methods in Econometrics: Theory and Practice (2006, background): “Wang and Rao (2002) develop a valid empirical likelihood ratio test in a missing data problem, where nonparametric imputation is used under the missing at random (MAR) assumption.”
Efficient and Doubly Robust Imputation for Covariate-Dependent Missing Responses (2008, methodology): “We apply empirical likelihood to impute missing data, whereas earlier approaches (e.g., Wang and Rao 2002; Wang et al. 2004) make empirical likelihood inference based on data imputed without using empirical likelihood.”
A review on empirical likelihood methods for regression (2009, methodology): “Using empirical likelihood for inference on the mean of the response variable when the response is subject to missingness at random was considered in Wang and Rao (2002) for a nonparametric regression model and Wang, Linton and Härdle (2004) for a semiparametric partially linear regression model.”
Empirical Likelihood Confidence Intervals for Response Mean with Data Missing at Random (2009, methodology): “With the non-parametric kernel regression imputation scheme, Wang & Rao (2002a) developed imputed empirical likelihood approaches for constructing confidence intervals of .”
Estimating Equations Inference With Missing Data (2008, background): “Recent studies include Aerts et al. (2002), Wang and Rao (2002), Chen, Ibrahim, and Shao (2004), Qi, Wang, and Prentice (2005), Lu and Copas (2005), among others.”
A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data (2011, result): “Wang and Rao (2002) also derived a result similar to (15).”
EMPIRICAL LIKELIHOOD FOR ESTIMATING EQUATIONS WITH MISSING VALUES (2009, methodology): “A popular imputation method is to impute a missing Yi by the conditional mean of Y given X = Xi as proposed in [36] under a parametric regression model and in [7] and [33] via the kernel estimator for the conditional mean.”
Estimating linear functionals in nonlinear regression with responses missing at random (2009, methodology): “In Wang and Rao (2002) they present an empirical likelihood approach for inference about the mean response in nonparametric regression, based on partial kernel regression imputation as suggested by Cheng (1994).”
Estimation in partially linear models with missing responses at random (2007, methodology): “In regression problems, commonly used imputation approaches include linear regression imputation [7] , nonparametric kernel regression imputation [3,22], semiparametric regression imputation [21], among others.”
Entropy Balancing is Doubly Robust (2015, background): “…popular in survey sampling (Deville and Särndal, 1992; Särndal and Lundström, 2005; Chan et al., 2015); (2) The empirical likelihood approach that significantly advances the theory of doubly robust estimation in observation study (Wang and Rao, 2002; Tan, 2006; Qin and Zhang, 2007; Tan, 2010).”