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Monte Carlo EM for Missing Covariates in Parametric Regression Models

Citations

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Cited by:

  1. Jiang, Wei & Josse, Julie & Lavielle, Marc, 2020. "Logistic regression with missing covariates—Parameter estimation, model selection and prediction within a joint-modeling framework," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
  2. James Y. Dai & Michael LeBlanc & Charles Kooperberg, 2009. "Semiparametric Estimation Exploiting Covariate Independence in Two-Phase Randomized Trials," Biometrics, The International Biometric Society, vol. 65(1), pages 178-187, March.
  3. Zhiwei Zhang & Howard Rockette, 2006. "Semiparametric Maximum Likelihood for Missing Covariates in Parametric Regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(4), pages 687-706, December.
  4. Lan Huang & Ming-Hui Chen & Joseph G. Ibrahim, 2005. "Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates," Biometrics, The International Biometric Society, vol. 61(3), pages 767-780, September.
  5. 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.
  6. 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.
  7. Niansheng Tang & Sy-Miin Chow & Joseph G. Ibrahim & Hongtu Zhu, 2017. "Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 875-903, December.
  8. Nicholas J. Horton & Nan M. Laird, 2001. "Maximum Likelihood Analysis of Logistic Regression Models with Incomplete Covariate Data and Auxiliary Information," Biometrics, The International Biometric Society, vol. 57(1), pages 34-42, March.
  9. Paul C. Lambert & Lucinda J. Billingham & Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams, 2008. "Estimating the cost‐effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 67-81, January.
  10. Ming‐Hui Chen & Joseph G. Ibrahim, 2001. "Maximum Likelihood Methods for Cure Rate Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 57(1), pages 43-52, March.
  11. Bernhardt Paul W., 2018. "Maximum Likelihood Estimation in a Semicontinuous Survival Model with Covariates Subject to Detection Limits," The International Journal of Biostatistics, De Gruyter, vol. 14(2), pages 1-16, November.
  12. Xiaoyan Shi & Hongtu Zhu & Joseph G. Ibrahim, 2009. "Local Influence for Generalized Linear Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1164-1174, December.
  13. Samiran Sinha & Krishna K. Saha & Suojin Wang, 2014. "Semiparametric approach for non-monotone missing covariates in a parametric regression model," Biometrics, The International Biometric Society, vol. 70(2), pages 299-311, June.
  14. Liang, Hua, 2008. "Generalized partially linear models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 880-895, May.
  15. Joseph Sexton & Petter Laake, 2009. "Stochastic Approximation Boosting for Incomplete Data Problems," Biometrics, The International Biometric Society, vol. 65(4), pages 1156-1163, December.
  16. S. J. Bonner & C. J. Schwarz, 2006. "An Extension of the Cormack–Jolly–Seber Model for Continuous Covariates with Application to Microtus pennsylvanicus," Biometrics, The International Biometric Society, vol. 62(1), pages 142-149, March.
  17. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
  18. Liang, Hua & Su, Haiyan & Zou, Guohua, 2008. "Confidence intervals for a common mean with missing data with applications in an AIDS study," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 546-553, December.
  19. Yang Zhao, 2021. "Semiparametric model for regression analysis with nonmonotone missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 461-475, June.
  20. Joseph G. Ibrahim & Hongtu Zhu & Ramon I. Garcia & Ruixin Guo, 2011. "Fixed and Random Effects Selection in Mixed Effects Models," Biometrics, The International Biometric Society, vol. 67(2), pages 495-503, June.
  21. Jakub Stoklosa & Wen-Han Hwang & David I Warton, 2023. "A general algorithm for error-in-variables regression modelling using Monte Carlo expectation maximization," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-21, April.
  22. Ib Thomsen & Li-Chun Zhang & Joseph Sexton, 2000. "Markov Chain Generated Profile Likelihood Inference under Generalized Proportional to Size Non-ignorable Non-response," Discussion Papers 274, Statistics Norway, Research Department.
  23. Chen, Xue-Dong & Fu, Ying-Zi, 2011. "Model selection for zero-inflated regression with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 765-773, January.
  24. Chen, Qingxia & Ibrahim, Joseph G. & Chen, Ming-Hui & Senchaudhuri, Pralay, 2008. "Theory and inference for regression models with missing responses and covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1302-1331, July.
  25. Hongtu Zhu & Joseph G. Ibrahim & Xiaoyan Shi, 2009. "Diagnostic Measures for Generalized Linear Models with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 686-712, December.
  26. Regier Michael D. & Moodie Erica E. M., 2016. "The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 65-77, May.
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