Maximum likelihood analysis of generalized linear models with missing covariates.

@article{Horton1999MaximumLA,
  title={Maximum likelihood analysis of generalized linear models with missing covariates.},
  author={Nicholas J. Horton and Nan M. Laird},
  journal={Statistical methods in medical research},
  year={1999},
  volume={8 1},
  pages={37-50}
}
Missing data is a common occurrence in most medical research data collection enterprises. There is an extensive literature concerning missing data, much of which has focused on missing outcomes. Covariates in regression models are often missing, particularly if information is being collected from multiple sources. The method of weights is an implementation of the EM algorithm for general maximum-likelihood analysis of regression models, including generalized linear models (GLMs) with incomplete… CONTINUE READING

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Missing covariates in generalized linear models when the missing data mechanism is nonignorable

  • JG Ibrahim, SR Lipsitz, M-H. Chen
  • Journal of the Royal Statistical Society, Series…
  • 1999

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