Imputation methods for quantile estimation under missing at random

Shu Yang, Jae Kwang Kim, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Imputation is frequently used to handle missing data for which multiple imputation is a popular technique. We propose a fractional hot deck imputation which produces a valid variance estimator for quantiles. In the proposed method, the imputed values are chosen from the set of respondents and are assigned with proper fractional weights that use a density function for the working model. In addition, we consider a nonparametric fractional imputation method based on nonparametric kernel regression, avoiding a parametric distribution assumption and thus giving more robustness. The resulting estimator can be called nonparametric fractionally imputation estimator. Valid variance estimation is also discussed. A limited simulation study compares the proposed methods favorably with other existing methods.

Original languageEnglish
Pages (from-to)369-377
Number of pages9
JournalStatistics and its Interface
Issue number3
StatePublished - 2013


  • Bahadur representation
  • Estimating equation
  • Fractional hot deck imputation
  • Jackknife variance estimator
  • Linearization method
  • Nonparametric imputation
  • Woodruff variance


Dive into the research topics of 'Imputation methods for quantile estimation under missing at random'. Together they form a unique fingerprint.

Cite this