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A fast imputation algorithm in quantile regression

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Abstract

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.

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Acknowledgements

The authors are thankful to the helpful discussions with Prof. Jae Kwang Kim. The authors gratefully acknowledge NIH awards R01 HG008980 and R03 HG007443, and NSF award DMS-120923.

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Authors and Affiliations

  1. National Academy of Innovation Strategy, China Association for Science and Technology, Beijing, China

    Hao Cheng

  2. School of Statistics, Renmin University of China, Beijing, China

    Hao Cheng

  3. Department of Biostatistics, Columbia University, New York, USA

    Hao Cheng & Ying Wei

Authors
  1. Hao Cheng
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  2. Ying Wei
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Correspondence to Hao Cheng.

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Cheng, H., Wei, Y. A fast imputation algorithm in quantile regression. Comput Stat 33, 1589–1603 (2018). https://doi.org/10.1007/s00180-018-0813-z

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  • DOI: https://doi.org/10.1007/s00180-018-0813-z

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