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Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data

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  • Andrew Gelman
  • Iven Van Mechelen
  • Geert Verbeke
  • Daniel F. Heitjan
  • Michel Meulders

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Suggested Citation

  • Andrew Gelman & Iven Van Mechelen & Geert Verbeke & Daniel F. Heitjan & Michel Meulders, 2005. "Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data," Biometrics, The International Biometric Society, vol. 61(1), pages 74-85, March.
    • Handle: RePEc:bla:biomet:v:61:y:2005:i:1:p:74-85
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2005.031010.x
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    References listed on IDEAS

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    1. J. S. Hodges, 1998. "Some algebra and geometry for hierarchical models, applied to diagnostics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 497-536.
    2. Geert Verbeke & Geert Molenberghs & Herbert Thijs & Emmanuel Lesaffre & Michael G. Kenward, 2001. "Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach," Biometrics, The International Biometric Society, vol. 57(1), pages 7-14, March.
    3. Geert Verbeke & Emmanuel Lesaffre, 1999. "The Effect of Drop‐Out on the Efficiency of Longitudinal Experiments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 363-375.
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    Cited by:

    1. Lei Jin & Suojin Wang, 2010. "A Model Validation Procedure when Covariate Data are Missing at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 403-421, September.
    2. Yang Zhao, 2022. "Diagnostic checking of multiple imputation models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 271-286, June.
    3. A. Mattei & F. Mealli, 2007. "Application of the Principal Stratification Approach to the Faenza Randomized Experiment on Breast Self-Examination," Biometrics, The International Biometric Society, vol. 63(2), pages 437-446, June.
    4. Michael J. Daniels & Arkendu S. Chatterjee & Chenguang Wang, 2012. "Bayesian Model Selection for Incomplete Data Using the Posterior Predictive Distribution," Biometrics, The International Biometric Society, vol. 68(4), pages 1055-1063, December.
    5. Yajuan Si & Jerome P. Reiter, 2013. "Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 499-521, October.
    6. Gunnhildur Högnadóttir Steinbakk & Geir Olve Storvik, 2009. "Posterior Predictive p‐values in Bayesian Hierarchical Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 320-336, June.
    7. Jung, Hyekyung & Schafer, Joseph L. & Seo, Byungtae, 2011. "A latent class selection model for nonignorably missing data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 802-812, January.
    8. Dimitris Rizopoulos & Geert Verbeke & Geert Molenberghs, 2010. "Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes," Biometrics, The International Biometric Society, vol. 66(1), pages 20-29, March.
    9. Brun, Mélanie & Abraham, Christophe & Jarry, Marc & Dumas, Jacques & Lange, Frédéric & Prévost, Etienne, 2011. "Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach," Ecological Modelling, Elsevier, vol. 222(5), pages 1069-1079.
    10. Zhongqi Liang & Qihua Wang & Yuting Wei, 2022. "Robust model selection with covariables missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(3), pages 539-557, June.
    11. Yucel, Recai M. & Demirtas, Hakan, 2010. "Impact of non-normal random effects on inference by multiple imputation: A simulation assessment," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 790-801, March.
    12. Sun, Zhihua & Wang, Qihua & Dai, Pengjie, 2009. "Model checking for partially linear models with missing responses at random," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 636-651, April.
    13. Meulders, Michel, 2013. "An R Package for Probabilistic Latent Feature Analysis of Two-Way Two-Mode Frequencies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i14).
    14. repec:jss:jstsof:45:i02 is not listed on IDEAS
    15. Yulei He & Trivellore E. Raghunathan, 2012. "Multiple imputation using multivariate gh transformations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(10), pages 2177-2198, June.
    16. Leonidas A. Zampetakis & Vassilis S. Moustakis, 2010. "An exploratory research on the factors stimulating corporate entrepreneurship in the Greek public sector," International Journal of Manpower, Emerald Group Publishing Limited, vol. 31(8), pages 871-887, November.
    17. Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.
    18. Mr. Michael Weber & Ms. Michaela Denk, 2011. "Avoid Filling Swiss Cheese with Whipped Cream: Imputation Techniques and Evaluation Procedures for Cross-Country Time Series," IMF Working Papers 2011/151, International Monetary Fund.

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