Nested multiple imputation of NMES via partially incompatible MCMC

The multiple imputation of the National Medical Expenditure Survey (NMES) involved the use of two new techniques, both having potentially broad applicability, and creating nested multiple imputations with their increased inferential efficiency.

Abstract

The multiple imputation of the National Medical Expenditure Survey (NMES) involved the use of two new techniques, both having potentially broad applicability. The first is to use distributionally incompatible MCMC (Markov Chain Monte Carlo), but to apply it only partially, to impute the missing values that destroy a monotone pattern, thereby limiting the extent of incompatibility. The second technique is to split the missing data into two parts, one that is much more computationally expensive to impute than the other, and create several imputations of the second part for each of the first part, thereby creating nested multiple imputations with their increased inferential efficiency.