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- Publisher
- Oxford University Press
- Copyright
- 2008 Biometrika Trust
- Subject
- Articles
- ISSN
- 0006-3444
- eISSN
- 1464-3510
- DOI
- 10.1093/biomet/asn042
- Publisher site
- See Article on Publisher Site
Abstract
When some of the records used to estimate the imputation models in multiple imputation are not used or available for analysis, the usual multiple imputation variance estimator has positive bias. We present an alternative approach that enables unbiased estimation of variances and, hence, calibrated inferences in such contexts. First, using all records, the imputer samples m values of the parameters of the imputation model. Second, for each parameter draw, the imputer simulates the missing values for all records n times. From these mn completed datasets, the imputer can analyse or disseminate the appropriate subset of records. We develop methods for interval estimation and significance testing for this approach. Methods are presented in the context of multiple imputation for measurement error.
Journal
Biometrika – Oxford University Press
Published: Dec 3, 2008
Keywords: Some key words Combining data Confidentiality Measurement error Missing data Multiple imputation