Missing Data Identification
Missing Data Identification is a systematic method within the field of data extraction that distinguishes itself by detecting and characterizing the absence of expected data elements in a dataset, thereby enabling subsequent handling of incomplete information.
293
Publications
38.7K
Citations
748
Authors
404
Institutions
Publications per year
2017–2026
77
Parents
293
Missing data: Our view of the state of the art.
Joseph L. Schafer, J. A. Graham · Psychological Methods · 2002 · 10.9K citations
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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University of Pennsylvania
Philadelphia, United States
5 h-index
9 publications
2.4K citations
University of Washington
Seattle, United States
5 h-index
9 publications
2.2K citations
University of Michigan
Ann Arbor, United States
5 h-index
12 publications
4.1K citations
Yale University
New Haven, United States
4 h-index
22 publications
1.7K citations

Harbin Institute of Technology
Harbin, China
4 h-index
9 publications
155 citations

University of the Witwatersrand
Johannesburg, South Africa
4 h-index
9 publications
132 citations
ETH Zurich
Zurich, Switzerland
3 h-index
7 publications
200 citations

McMaster University
Hamilton, Canada
3 h-index
14 publications
1.3K citations
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8 publications · 547 citations
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CSDA · 7 publications · 321 citations
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6 publications · 652 citations
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Repository · 5 publications · 138 citations
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5 publications · 13K citations
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