· 2012
Flexible Imputation of Missing Data
- 2,026citations
- 2012year
Short summary
Multiple imputation fills missing data with plausible values, incorporating uncertainty, and addresses multiple statistical problems.
AI-generated from the title and abstract; the full text is not read.
Key points
- Missing data is a common problem in all scientific fields.
- Multiple imputation is a key statistical method for handling missing data.
- The technique fills data gaps with plausible values.
- Uncertainty from imputation is preserved within the data.
- Multiple imputation can solve various other statistical problems.
AI-generated from the title and abstract; the full text is not read.
Abstract
Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science-multiple imputation-fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which a
The authors' abstract, as published at the source., 2012 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics