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Multiple imputation and other resampling schemes for imputing missing observations

Author

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  • Srivastava, Muni S.
  • Dolatabadi, Mohammad

Abstract

The problem of imputing missing observations under the linear regression model is considered. It is assumed that observations are missing at random and all the observations on the auxiliary or independent variables are available. Estimates of the regression parameters based on singly and multiply imputed values are given. Jackknife as well as bootstrap estimates of the variance of the singly imputed estimator of the regression parameters are given. These estimators are shown to be consistent estimators. The asymptotic distributions of the imputed estimators are also given to obtain interval estimates of the parameters of interest. These interval estimates are then compared with the interval estimates obtained from multiple imputation. It is shown that singly imputed estimators perform at least as good as multiply imputed estimators. A new nonparametric multiply imputed estimator is proposed and shown to perform as good as a multiply imputed estimator under normality. The singly imputed estimator, however, still remains at least as good as a multiply imputed estimator.

Suggested Citation

  • Srivastava, Muni S. & Dolatabadi, Mohammad, 2009. "Multiple imputation and other resampling schemes for imputing missing observations," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1919-1937, October.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:9:p:1919-1937
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    Cited by:

    1. Subramanian, Sundarraman, 2011. "Multiple imputations and the missing censoring indicator model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 105-117, January.
    2. Jamshidian, Mortaza & Jalal, Siavash & Jansen, Camden, 2014. "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i06).

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