Nonparametric Markov chain bootstrap for multiple imputation

Multiple imputation is a statistical method for analyzing data with missing values. Nonparametric Markov chain bootstrap methods can be used to generate multiple imputations of both scalar and multivariate outcome variables, under the assumption that the data are missing completely at random, and nonparametric inference can be obtained using multiple implementation bootstrap. The nonparametric approach is useful when parametric settings are inappropriate or di3cult. An extension of the Markov chain bootstrap method is discussed under a more complex nonresponse assumption. c © 2003 Elsevier B.V. All rights reserved.