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A hybrid approach for regression analysis with block missing data

Author

Listed:
  • Li, Zhengbang
  • Li, Qizhai
  • Han, Chien-Pai
  • Li, Bo

Abstract

Missing data often arise in practice. The commonly employed approach to handle the missing data is imputation, which is effective when the missing mechanism is known and each subject in the data set misses at random. However, the situation where the imputation is not appropriate often emerged. Because in that situation, some data are not missing at random, so a hybrid estimate, where the Bayesian and frequentist approaches are used for inferring the parameters with and without prior information respectively, is proposed. The asymptotic properties of the hybrid estimator are also provided. Numerical results including simulation studies and data analysis about grade point average (GPA) are conducted to show the performances of the proposed method.

Suggested Citation

  • Li, Zhengbang & Li, Qizhai & Han, Chien-Pai & Li, Bo, 2014. "A hybrid approach for regression analysis with block missing data," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 239-247.
    • Handle: RePEc:eee:csdana:v:75:y:2014:i:c:p:239-247
    DOI: 10.1016/j.csda.2014.02.014
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    References listed on IDEAS

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    1. D. A. Ratkowsky, 1974. "Maximum Likelihood Estimation in Small Incomplete Samples from the Bivariate Normal Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(2), pages 180-189, June.
    2. Joseph Sexton & Petter Laake, 2009. "Stochastic Approximation Boosting for Incomplete Data Problems," Biometrics, The International Biometric Society, vol. 65(4), pages 1156-1163, December.
    3. Stuart R. Lipsitz & Joseph G. Ibrahim & Garrett M. Fitzmaurice, 1999. "Likelihood Methods for Incomplete Longitudinal Binary Responses with Incomplete Categorical Covariates," Biometrics, The International Biometric Society, vol. 55(1), pages 214-223, March.
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