Multiple Imputation
Multiple Imputation is a statistical technique for handling missing data that belongs to the class of imputation methods, distinguished by generating multiple plausible values for each missing entry to reflect uncertainty, thereby producing several complete datasets that are analyzed separately and combined to yield valid inferences.
2.9K
Publications
313.8K
Citations
11.4K
Authors
3.3K
Institutions
Publications per year
2017–2026
1.2K
2.9K
Missing data: Our view of the state of the art.
Joseph L. Schafer, J. A. Graham · Psychological Methods · 2002 · 10.9K citations
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11.4K
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London School of Hygiene & Tropical Medicine, United Kingdom
44 publications · 12.7K citations
32
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24
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23
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1–5 of 11.4K
3.3K
University College London
London, United Kingdom
52 h-index
337 publications
40.9K citations
London School of Hygiene & Tropical Medicine
London, United Kingdom
52 h-index
196 publications
31.3K citations
Harvard University
Cambridge, United States
45 h-index
192 publications
44K citations
MRC Clinical Trials Unit at UCL
London, United Kingdom
43 h-index
81 publications
31.3K citations
University of Michigan
Ann Arbor, United States
35 h-index
160 publications
13.5K citations

University of Bristol
Bristol, United Kingdom
35 h-index
183 publications
42K citations
MRC Biostatistics Unit
Cambridge, United Kingdom
35 h-index
70 publications
26.9K citations
University of California, Los Angeles
Los Angeles, United States
34 h-index
107 publications
9.7K citations
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158 publications · 23.2K citations
56
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62 publications · 8.9K citations
39
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58 publications · 12.9K citations
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76 publications · 4K citations
31
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52 publications · 8.2K citations
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