Dealing with missing data via parametric multiple imputation methods usually implies stating
several strong assumptions both about the distribution of the data and about underlying …

Multiple imputation (MI) is one of the most popular methods to deal with missing data, and its
use has been rapidly increasing in medical studies. Although MI is rather appealing in …

Multiple imputation is a technique for handling data sets with missing values. The method
fills in the missing values several times, creating several completed data sets for analysis …

Imputation is a much used method for handling missing data. It is appealing as it separates
the missing data part of the analysis, which is handled by imputation, and the estimation …

In this paper, we describe how to use multiple imputation semiparametrically to obtain
estimates of parameters and their standard errors when some individuals have missing data …

Conventional multiple imputation (MI)(Rubin, 1987) replaces the missing values in a dataset
by m> 1 sets of simulated values. We describe a two-stage extension of MI in which the …

Statistical analysis with missing data has always been a challenge. However, there have
been tremendous advances in statistical theory related to analysis with missing data. Of …

Imputation offers an effective solution to the problem of missing values. We propose a
nonparametric multiple imputation procedure that uses multiple outcome regression models …

Multiple imputation is a well-established general technique for analyzing data with missing
values. A convenient way to implement multiple imputation is sequential regression multiple …

This chapter offers an accessible introduction to missing-data procedures, especially for
longitudinal data, using multiple imputation (Rubin, 1987; Shafer, 1997a), which is the …