期刊
BIOMETRIKA
卷 101, 期 1, 页码 155-173出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/ast044
关键词
Chained equation; Convergence; Iterative imputation; Markov chain
资金
- National Science Foundation
- Wang Xuelian foundation
Iterative imputation, in which variables are imputed one at a time conditional on all the others, is a popular technique that can be convenient and flexible, as it replaces a potentially difficult multivariate modelling problem with relatively simple univariate regressions. In this paper, we begin to characterize the stationary distributions of iterative imputations and their statistical properties, accounting for the conditional models being iteratively estimated from data rather than being prespecified. When the families of conditional models are compatible, we provide sufficient conditions under which the imputation distribution converges in total variation to the posterior distribution of a Bayesian model. When the conditional models are incompatible but valid, we show that the combined imputation estimator is consistent.
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