Handling Data with Three Types of Missing Values

作者: Jennifer Boyko

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参考文章(24)
Xiao-Li Meng, Multiple-Imputation Inferences with Uncongenial Sources of Input Statistical Science. ,vol. 9, pp. 538- 558 ,(1994) , 10.1214/SS/1177010269
Hakan Demirtas, Joseph L. Schafer, On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out. Statistics in Medicine. ,vol. 22, pp. 2553- 2575 ,(2003) , 10.1002/SIM.1475
Ian R. White, John B. Carlin, Bias and efficiency of multiple imputation compared with complete‐case analysis for missing covariate values Statistics in Medicine. ,vol. 29, pp. 2920- 2931 ,(2010) , 10.1002/SIM.3944
J Barnard, Small-sample degrees of freedom with multiple imputation Biometrika. ,vol. 86, pp. 948- 955 ,(1999) , 10.1093/BIOMET/86.4.948
Jerome P Reiter, Trivellore E Raghunathan, The Multiple Adaptations of Multiple Imputation Journal of the American Statistical Association. ,vol. 102, pp. 1462- 1471 ,(2007) , 10.1198/016214507000000932
Roderick JA Little, Donald B Rubin, None, Statistical Analysis with Missing Data ,(1987)
Aki Vehtari, David B. Dunson, Andrew Gelman, Hal S. Stern, Donald B. Rubin, John B. Carlin, Bayesian Data Analysis ,(1995)
Juned Siddique, Ofer Harel, Catherine M. Crespi, Addressing Missing Data Mechanism Uncertainty using Multiple-Model Multiple Imputation: Application to a Longitudinal Clinical Trial The Annals of Applied Statistics. ,vol. 6, pp. 1814- 1837 ,(2012) , 10.1214/12-AOAS555
A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum Likelihood from Incomplete Data Via theEMAlgorithm Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 39, pp. 1- 22 ,(1977) , 10.1111/J.2517-6161.1977.TB01600.X