Misclassification Models
Misclassification Models is a statistical modeling framework within Bayesian measurement error models that specifically accounts for systematic errors in categorical variable classification by incorporating probabilistic representations of misassignment to distinguish true underlying states from observed erroneous classifications.
832
Publications
46.7K
Citations
2.5K
Authors
1K
Institutions
Publications per year
2017–2026
154
Variants
Misclassification Error Models
832
Estimation of Error Rates in Discriminant Analysis
Peter A. Lachenbruch, M. R. Mickey · Technometrics · 1968 · 1.5K citations
Mei‐Ling Ting Lee, Frank C. Kuo, G. À. Whitmore et al. · Proceedings of the National Academy of Sciences · 2000 · 870 citations
1–5 of 832
2.5K
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University of California, Los Angeles, United States
10 publications · 2.4K citations
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1K
Harvard University
Cambridge, United States
23 h-index
68 publications
6.1K citations

Johns Hopkins University
Baltimore, United States
18 h-index
61 publications
4.2K citations
University of California, Los Angeles
Los Angeles, United States
15 h-index
25 publications
5.7K citations
University of North Carolina at Chapel Hill
Chapel Hill, United States
13 h-index
31 publications
1.4K citations
University of Washington
Seattle, United States
13 h-index
32 publications
1.4K citations
Emory University
Atlanta, United States
12 h-index
31 publications
1.2K citations
Florida Atlantic University
Boca Raton, United States
11 h-index
21 publications
1.1K citations

University of Waterloo
Waterloo, Canada
11 h-index
23 publications
865 citations
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29 publications · 2K citations
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41 publications · 1.3K citations
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24 publications · 1.4K citations
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25 publications · 2K citations
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AJE · 20 publications · 2.7K citations
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