About us

Quantile Imputation
Quantile imputation is a multiple imputation method based on the estimation of conditional quantiles of missing observations given the observed data. The method does not require modeling a likelihood and has desirable features that may be useful in some practical settings. It can also be applied to impute dependent, bounded, censored, and count data.
Zhen H. Multiple Imputation Based on Conditional Quantile Estimation. 2008, Doctoral Thesis, University of South Carolina.

Bottai M and Zhen H. Multiple Imputation Based on Conditional Quantile Estimation. Epid, Biostat, Pub Health, 2013, 10, No 1.

The following Stata command will appear soon:

"qimpute" method for "mi impute"
November 11, 2011
Quantile imputation of missing data
4th Nordic and Baltic Stata Users Group meeting. Stockholm, Sweden

Selected Applications
Liu M, Daniels MJ, Perri MG. Quantile regression in the presence of monotone missingness with sensitivity analysis. Biostatistics, 2016, 17(1):108-121.

Geraci M. Estimation of regression quantiles in complex surveys with data missing at random: an application to birthweight determinants. Stat Methods Med Research, 2014

Unit of Biostatistics, Nobels väg 13, Karolinska Institutet, 17177 Stockholm, Sweden