Missing Data Diagnostics
Missing Data Diagnostics is a systematic methodological framework used to assess the patterns, mechanisms, and implications of missing data within a dataset, distinguishing itself by providing formal criteria and analytical tools to determine the nature and extent of missingness in order to guide appropriate handling strategies.
53
Publications
4.7K
Citations
151
Authors
92
Institutions
Publications per year
2017–2026
9
53
pcaMethods—a bioconductor package providing PCA methods for incomplete data
Wolfram Stacklies, Henning Redestig, Matthias Scholz et al. · Bioinformatics · 2007 · 1.2K citations · Full text
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92
University of North Carolina at Chapel Hill
Chapel Hill, United States
3 h-index
7 publications
105 citations

University of Windsor
Windsor, Canada
2 h-index
6 publications
284 citations
Yale University
New Haven, United States
2 h-index
7 publications
404 citations
Tsinghua University
Beijing, China
2 h-index
2 publications
661 citations
University of Oxford
Oxford, United Kingdom
2 h-index
5 publications
239 citations
KU Leuven
Leuven, Belgium
2 h-index
5 publications
495 citations
New York University
New York, United States
2 h-index
3 publications
66 citations
University of Connecticut
Storrs, United States
2 h-index
2 publications
20 citations
1–8 of 92
4 publications · 218 citations
4
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2 publications · 134 citations
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2 publications · 94 citations
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JOM · 2 publications · 57 citations
2
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2 publications · 22 citations
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1–5