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Subsample ignorable likelihood for regression analysis with missing data

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  • Roderick J. Little
  • Nanhua Zhang

Abstract

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Suggested Citation

  • Roderick J. Little & Nanhua Zhang, 2011. "Subsample ignorable likelihood for regression analysis with missing data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(4), pages 591-605, August.
    • Handle: RePEc:bla:jorssc:v:60:y:2011:i:4:p:591-605
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    Cited by:

    1. Matthias von Davier & Youngmi Cho & Tianshu Pan, 2019. "Effects of Discontinue Rules on Psychometric Properties of Test Scores," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 147-163, March.
    2. Sophia Rabe-Hesketh & Anders Skrondal, 2023. "Ignoring Non-ignorable Missingness," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 31-50, March.
    3. Nanhua Zhang & Roderick J. Little, 2012. "A Pseudo-Bayesian Shrinkage Approach to Regression with Missing Covariates," Biometrics, The International Biometric Society, vol. 68(3), pages 933-942, September.
    4. Yi He & Linzhi Zheng & Peng Luo, 2023. "Treatment Benefit and Treatment Harm Rates with Nonignorable Missing Covariate, Endpoint, or Treatment," Mathematics, MDPI, vol. 11(21), pages 1-18, October.
    5. Zhang Nanhua & Chen Henian & Elliott Michael R., 2016. "Nonrespondent Subsample Multiple Imputation in Two-Phase Sampling for Nonresponse," Journal of Official Statistics, Sciendo, vol. 32(3), pages 769-785, September.
    6. Jing Sun, 2020. "An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data," Computational Statistics, Springer, vol. 35(4), pages 1621-1636, December.

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