You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. 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
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
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.
Preview not available
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