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Large-Sample Theory for Parametric Multiple Imputation Procedures
Naisyin Wang and James M. Robins
Vol. 85, No. 4 (Dec., 1998), pp. 935-948
Stable URL: http://www.jstor.org/stable/2337494
Page Count: 14
You can always find the topics here!Topics: Estimators, Simulations, Statistical variance, Statistical estimation, Preliminary estimates, Consistent estimators, Data imputation, Statism, Missing data, Parametric models
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We consider the asymptotic behaviour of various parametric multiple imputation procedures which include but are not restricted to the `proper' imputation procedures proposed by Rubin (1978). The asymptotic variance structure of the resulting estimators is provided. This result is used to compare the relative efficiencies of different imputation procedures. It also provides a basis to understand the behaviour of two Monte Carlo iterative estimators, stochastic EM (Celeux & Diebolt, 1985; Wei & Tanner, 1990) and simulated EM (Ruud, 1991). We further develop properties of these estimators when they stop at iteration K with imputation size m. An application to a measurement error problem is used to illustrate the results.
Biometrika © 1998 Biometrika Trust