Jackknife empirical likelihood method for multiply robust estimation with missing data-Reference-Cited by-同舟云学术

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Jackknife empirical likelihood method for multiply robust estimation with missing data

Published:2018-11 Issue: Volume:127 Page:258-268
ISSN:0167-9473
Container-title:Computational Statistics & Data Analysis
language:en
Short-container-title:Computational Statistics & Data Analysis

Author:

Chen Sixia,Haziza David

Funder

National Institutes of Health, National Institute of General Medical Sciences

Natural Sciences and Engineering Research Council

Canadian Statistical Sciences Institute

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Statistics and Probability

Reference27 articles.

1. Doubly robust estimation in missing data and causal inference models;Bang;Biometrics,2005

2. Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data;Cao;Biometrika,2009

3. Oracle, multiple robust and multipurpose calibration in missing response problem;Chan;Statist. Sci.,2014

4. On bartlett correction of empirical likelihood in the presence of nuisance parameters;Chen;Biometrika,2006

5. Multiply robust imputation procedures for the treatment of item nonresponse in surveys;Chen;Biometrika,2017

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1. Bayesian jackknife empirical likelihood‐based inference for missing data and causal inference;Canadian Journal of Statistics;2024-08

2. A new portmanteau test for predictive regression models with possible embedded endogeneity;Journal of Time Series Analysis;2024-05-12

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4. A review of recent advances in empirical likelihood;WIREs Computational Statistics;2022-09-20

5. Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference;International Statistical Review;2022-08-11

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