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Large-Sample Theory for Parametric Multiple Imputation Procedures

Naisyin Wang and James M. Robins
Biometrika
Vol. 85, No. 4 (Dec., 1998), pp. 935-948
Published by: Oxford University Press on behalf of Biometrika Trust
Stable URL: http://www.jstor.org/stable/2337494
Page Count: 14
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Large-Sample Theory for Parametric Multiple Imputation Procedures
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Abstract

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.

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