Abstract #301780

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JSM 2003 Abstract #301780
Activity Number: 333
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #301780
Title: Variance Estimate on Multiple Imputation Procedures for Missing Data
Author(s): Hung Chen*+
Companies: National Taiwan University
Address: Dept. of Mathematics, Taipei, , , Taiwan
Keywords: incomplete data ; multiple imputation ; EM algorithm
Abstract:

In observational studies, data are often missing. To handle the missing data, we can employ the EM algorithm (Dempster, Laird, and Rubin 1977) to find maximum likelihood estimate of unknown parameters. Or we can use multiple imputation (Rubin 1987) to "fill in" the missing data. Wang and Robins (1998) gave the asymptotic variance of estimated parameter obtained by the method of multiple imputation. The derived variance consists of three components. They correspond to the variability due to maximum likelihood estimate based on observed data, estimated posterior distribution, and imputed values. In Rubin's (1987) method for estimating variance used in multiple imputation inference, it consists of two components instead of three. In this paper, we propose an estimate of variance with three components which corresponds to the result presented in Wang and Robins (1998). This proposed estimate gives estimates on these three variance components. This can be useful in devising the number of imputation when the same person works on imputation and data analysis at the same time.

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