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Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates

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  • Lan Huang
  • Ming-Hui Chen
  • Joseph G. Ibrahim

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  • 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.
    • Handle: RePEc:bla:biomet:v:61:y:2005:i:3:p:767-780
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2005.00338.x
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    References listed on IDEAS

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    1. J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
    2. Amy L. Stubbendick & Joseph G. Ibrahim, 2003. "Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models," Biometrics, The International Biometric Society, vol. 59(4), pages 1140-1150, December.
    3. Michael J. Daniels, 2002. "Bayesian analysis of covariance matrices and dynamic models for longitudinal data," Biometrika, Biometrika Trust, vol. 89(3), pages 553-566, August.
    4. Ming-Hui Chen, 2004. "Bayesian criterion based model assessment for categorical data," Biometrika, Biometrika Trust, vol. 91(1), pages 45-63, March.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz, 1999. "Monte Carlo EM for Missing Covariates in Parametric Regression Models," Biometrics, The International Biometric Society, vol. 55(2), pages 591-596, June.
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    Cited by:

    1. 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.
    2. Serguei Rouzinov & André Berchtold, 2022. "Regression-Based Approach to Test Missing Data Mechanisms," Data, MDPI, vol. 7(2), pages 1-28, January.
    3. Chen, Ming-Hui & Ibrahim, Joseph G. & Shao, Qi-Man, 2009. "Maximum likelihood inference for the Cox regression model with applications to missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2018-2030, October.
    4. 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.
    5. Joseph Ibrahim & Geert Molenberghs, 2009. "Missing data methods in longitudinal studies: a review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 1-43, May.
    6. Z. I. Kalaylioglu & O. Ozturk, 2013. "Bayesian semiparametric models for nonignorable missing mechanisms in generalized linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1746-1763, August.
    7. Lyubov Doroshenko & Brunero Liseo, 2023. "Generalized linear mixed model with bayesian rank likelihood," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 425-446, June.

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