Robust propensity score weighting estimation under missing at random-Reference-Cited by-同舟云学术

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Robust propensity score weighting estimation under missing at random

Published:2024-01-01 Issue:2 Volume:18 Page:
ISSN:1935-7524
Container-title:Electronic Journal of Statistics
language:
Short-container-title:Electron. J. Statist.

Author:

Wang Hengfang¹,Kim Jae Kwang²,Han Jeongseop³,Lee Youngjo³

Affiliation:

1. School of Mathematics and Statistics & Fujian Provincial Key Laboratory of Statistics and Artificial Intelligence, Fujian Normal University, Fuzhou, Fujian, 350117, China

2. Department of Statistics, Iowa State University, Ames, IA, 50011, U.S.A

3. Department of Statistics, Seoul National University, Seoul, 08826, Korea (South)

Publisher

Institute of Mathematical Statistics

Reference43 articles.

1. HAN, P. and WANG, L. (2013). Estimation with missing data: Beyond double robustness. Biometrika 100 417–430.

2. BANG, H. and ROBINS, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics 61 962–973.

3. CAO, W., TSIATIS, A. A. and DAVIDIAN, M. (2009). Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data. Biometrika 96 723–734.

4. ROBINS, J. M., ROTNITZKY, A. and ZHAO, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American statistical Association 89 846–866.

5. NEWEY, W. K. and MCFADDEN, D. (1994). Large sample estimation and hypothesis testing. Handbook of Econometrics 4 2111–2245.

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