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Imputation and Variable Selection in Linear Regression Models with Missing Covariates

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  • Xiaowei Yang
  • Thomas R. Belin
  • W. John Boscardin

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  • Xiaowei Yang & Thomas R. Belin & W. John Boscardin, 2005. "Imputation and Variable Selection in Linear Regression Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 61(2), pages 498-506, June.
    • Handle: RePEc:bla:biomet:v:61:y:2005:i:2:p:498-506
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2005.00317.x
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    References listed on IDEAS

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    1. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
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    Cited by:

    1. Yunxi Zhang & Soeun Kim, 2024. "Gaussian Graphical Model Estimation and Selection for High-Dimensional Incomplete Data Using Multiple Imputation and Horseshoe Estimators," Mathematics, MDPI, vol. 12(12), pages 1-15, June.
    2. Adriano Zanin Zambom & Gregory J. Matthews, 2021. "Sure independence screening in the presence of missing data," Statistical Papers, Springer, vol. 62(2), pages 817-845, April.
    3. Michael Bergrab & Christian Aßmann, 2024. "Automated Bayesian variable selection methods for binary regression models with missing covariate data," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 18(2), pages 203-244, June.
    4. Nitzan Cohen & Yakir Berchenko, 2021. "Normalized Information Criteria and Model Selection in the Presence of Missing Data," Mathematics, MDPI, vol. 9(19), pages 1-23, October.
    5. Consentino, Fabrizio & Claeskens, Gerda, 2010. "Order selection tests with multiply imputed data," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2284-2295, October.

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