Study of imputation procedures for nonparametric density estimation based on missing censored lifetimes

Imputation is a standard procedure in dealing with missing data and there are many competing imputation methods. It is proposed to analyze imputation procedures via comparison with a benchmark developed by the asymptotic theory. Considered model is nonparametric density estimation of the missing right censored lifetime of interest. This model is of a special interest for understanding imputation because each underlying observation is the pair of censored lifetime and indicator of censoring. The latter creates a number of interesting scenarios and challenges for imputation when best methods may or may not be applicable. Further, the theory sheds light on why the effect of imputation depends on an underlying density. The methodology is tested on real life datasets and via intensive simulations. Data and R code are provided.