Empirical likelihood method for non-ignorable missing data problems
Author
Abstract
Suggested Citation
DOI: 10.1007/s10985-016-9381-0
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Peisong Han & Lu Wang, 2013. "Estimation with missing data: beyond double robustness," Biometrika, Biometrika Trust, vol. 100(2), pages 417-430.
- Zhong, Ping-Shou & Chen, Sixia, 2014. "Jackknife empirical likelihood inference with regression imputation and survey data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 193-205.
- Qihua Wang & Pengjie Dai, 2008. "Semiparametric model-based inference in the presence of missing responses," Biometrika, Biometrika Trust, vol. 95(3), pages 721-734.
- repec:mpr:mprres:8160 is not listed on IDEAS
- Jing Qin & Biao Zhang, 2007. "Empirical‐likelihood‐based inference in missing response problems and its application in observational studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 101-122, February.
- Cheng Yong Tang & Yongsong Qin, 2012. "An efficient empirical likelihood approach for estimating equations with missing data," Biometrika, Biometrika Trust, vol. 99(4), pages 1001-1007.
- Gong Tang, 2003. "Analysis of multivariate missing data with nonignorable nonresponse," Biometrika, Biometrika Trust, vol. 90(4), pages 747-764, December.
- Jae Kwang Kim & Jongho Im, 2014. "Propensity score adjustment with several follow-ups," Biometrika, Biometrika Trust, vol. 101(2), pages 439-448.
- Kim, Jae Kwang & Yu, Cindy Long, 2011. "A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 157-165.
- Sheng Wang & Jun Shao & Jae Kwang Kim, "undated". "An Instrumental Variable Approach for Identification and Estimation with Nonignorable Nonresponse," Mathematica Policy Research Reports a9593fac2c9746f486d2162f9, Mathematica Policy Research.
- Zhiqiang Tan, 2010. "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, Biometrika Trust, vol. 97(3), pages 661-682.
- Nevo, Aviv, 2003.
"Using Weights to Adjust for Sample Selection When Auxiliary Information Is Available,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 43-52, January.
- Aviv Nevo, 2001. "Using Weights to Adjust for Sample Selection When Auxiliary Information is Available," NBER Technical Working Papers 0275, National Bureau of Economic Research, Inc.
- Zhou, Yong & Wan, Alan T. K & Wang, Xiaojing, 2008. "Estimating Equations Inference With Missing Data," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1187-1199.
- Cheng Yong Tang & Chenlei Leng, 2011. "Empirical likelihood and quantile regression in longitudinal data analysis," Biometrika, Biometrika Trust, vol. 98(4), pages 1001-1006.
- Kung‐Yee Liang & Jing Qin, 2000. "Regression analysis under non‐standard situations: a pairwise pseudolikelihood approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 773-786.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Tianqing Liu & Xiaohui Yuan, 2020. "Doubly robust augmented-estimating-equations estimation with nonignorable nonresponse data," Statistical Papers, Springer, vol. 61(6), pages 2241-2270, December.
- Sima Sharghi & Kevin Stoll & Wei Ning, 2024. "Statistical inferences for missing response problems based on modified empirical likelihood," Statistical Papers, Springer, vol. 65(7), pages 4079-4120, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Peisong Han, 2016. "Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 246-260, March.
- Xiaogang Duan & Guosheng Yin, 2017. "Ensemble Approaches to Estimating the Population Mean with Missing Response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 899-917, December.
- Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
- Guo, Xu & Song, Lianlian & Fang, Yun & Zhu, Lixing, 2019. "Model checking for general linear regression with nonignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 1-12.
- Liu, Tianqing & Yuan, Xiaohui & Sun, Jianguo, 2021. "Weighted rank estimation for nonparametric transformation models with nonignorable missing data," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
- Puying Zhao & Hui Zhao & Niansheng Tang & Zhaohai Li, 2017. "Weighted composite quantile regression analysis for nonignorable missing data using nonresponse instrument," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 189-212, April.
- Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
- Xuerong Chen & Alan T. K. Wan & Yong Zhou, 2015. "Efficient Quantile Regression Analysis With Missing Observations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 723-741, June.
- Kwun Chuen Gary Chan & Sheung Chi Phillip Yam & Zheng Zhang, 2016. "Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 673-700, June.
- Han, Peisong & Song, Peter X.-K. & Wang, Lu, 2015. "Achieving semiparametric efficiency bound in longitudinal data analysis with dropouts," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 59-70.
- Xuerong Chen & Guoqing Diao & Jing Qin, 2020. "Pseudo likelihood‐based estimation and testing of missingness mechanism function in nonignorable missing data problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1377-1400, December.
- Jiwei Zhao, 2017. "Reducing bias for maximum approximate conditional likelihood estimator with general missing data mechanism," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(3), pages 577-593, July.
- Satoshi Hattori & Masayuki Henmi, 2014. "Stratified doubly robust estimators for the average causal effect," Biometrics, The International Biometric Society, vol. 70(2), pages 270-277, June.
- Cui, Li-E & Zhao, Puying & Tang, Niansheng, 2022. "Generalized empirical likelihood for nonsmooth estimating equations with missing data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
- Peisong Han & Linglong Kong & Jiwei Zhao & Xingcai Zhou, 2019. "A general framework for quantile estimation with incomplete data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 305-333, April.
- Cui, Xia & Guo, Jianhua & Yang, Guangren, 2017. "On the identifiability and estimation of generalized linear models with parametric nonignorable missing data mechanism," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 64-80.
- Zhong, Ping-Shou & Chen, Sixia, 2014. "Jackknife empirical likelihood inference with regression imputation and survey data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 193-205.
- Zhang, Jing & Wang, Qihua & Kang, Jian, 2020. "Feature screening under missing indicator imputation with non-ignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
- Jiang, Depeng & Zhao, Puying & Tang, Niansheng, 2016. "A propensity score adjustment method for regression models with nonignorable missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 98-119.
- Yujing Shao & Lei Wang, 2022. "Generalized partial linear models with nonignorable dropouts," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 223-252, February.
More about this item
Keywords
Constrained estimation; Empirical likelihood; Non-ignorable missing data; Survey sampling;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lifeda:v:23:y:2017:i:1:d:10.1007_s10985-016-9381-0. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.