A fast imputation algorithm in quantile regression

Hao Cheng () and Ying Wei ()
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Hao Cheng: China Association for Science and Technology
Ying Wei: Columbia University

Computational Statistics, 2018, vol. 33, issue 4, No 2, 1589-1603

Abstract: 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.

Keywords: Missing data; Inverse probability weighting; Quantile regression; Imputation methods (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-018-0813-z

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