Multiple Edit/Multiple Imputation for Multivariate Continuous Data
| Autor: | Joseph L. Schafer, Bonnie Ghosh-Dastidar |
|---|---|
| Rok vydání: | 2003 |
| Předmět: |
Statistics and Probability
Multivariate statistics Multivariate analysis Observational error Statistics::Applications Markov chain Monte Carlo computer.software_genre Missing data symbols.namesake Outlier symbols Statistics::Methodology Imputation (statistics) Data mining Statistics Probability and Uncertainty computer Gibbs sampling Mathematics |
| Zdroj: | Journal of the American Statistical Association. 98:807-817 |
| ISSN: | 1537-274X 0162-1459 |
| DOI: | 10.1198/016214503000000738 |
| Popis: | Multiple imputation replaces an incomplete dataset with m > 1 simulated complete versions that are analyzed separately by standard methods. We present a natural extension of multiple imputation for handling the dual problems of nonresponse and response error. This extension, which we call multiple edit/multiple imputation (MEMI), replaces an observed dataset containing missing values and errors with m > 1 simulated versions of the ideal dataset that is complete and error-free. These ideal data sets are analyzed separately, and the results are combined using the same rules as for multiple imputation. The resulting inferences simultaneously reflect uncertainty due to nonresponse and response error. MEMI may be an attractive alternative to deterministic or quasi-statistical edit and imputation procedures used by many data-collecting agencies. Producing MEMI's requires assumptions about the distribution of the ideal data, the nature of nonresponse, and a model for the response error mechanism. However, fittin... |
| Databáze: | OpenAIRE |
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