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A fast imputation algorithm in quantile regression.

Hao ChengYing Wei
Published in: Computational statistics (2018)
In many applications, some covariates could be missing for various reasons. Regression quantiles could be either biased or under-powered when ignoring the missing data. Multiple imputation and EM-based augment approach have been proposed to fully utilize the data with missing covariates for quantile regression. Both methods however are computationally expensive. We propose a fast imputation algorithm (FI) to handle the missing covariates in quantile regression, which is an extension of the fractional imputation in likelihood based regressions. FI and modified imputation algorithms (FIIPW and MIIPW) are compared to existing MI and IPW approaches in the simulation studies, and applied to part of of the National Collaborative Perinatal Project study.
Keyphrases
  • machine learning
  • quality improvement
  • deep learning
  • electronic health record
  • big data
  • pregnant women
  • artificial intelligence
  • neural network