Bayesian Measurement Error Models
Bayesian Measurement Error Models is a statistical modeling framework that belongs to the class of Bayesian hierarchical models and specifically accounts for uncertainty due to inaccuracies in observed measurements by incorporating prior distributions on true but unobserved covariates or predictors.
208
Publications
10.5K
Citations
580
Authors
344
Institutions
Publications per year
2017–2026
52
Parents
Children
Bayesian Error-in-variables ModelsClassical Additive Error ModelsClassical Measurement ErrorDependent Measurement ErrorDifferential Measurement ErrorFunctional Measurement Error ModelsHierarchical Measurement Error ModelsLongitudinal Measurement Error ModelsMisclassification ModelsMismeasured Covariates
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1–5 of 208
580
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Laboratoire des Sciences du Climat et de l'Environnement, France
5 publications · 311 citations
5
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4
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4
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1–5 of 580
344
University of Michigan
Ann Arbor, United States
9 h-index
14 publications
511 citations

University of British Columbia
Vancouver, Canada
9 h-index
13 publications
394 citations
Texas A&M University
College Station, United States
8 h-index
15 publications
563 citations

University of Georgia
Athens, United States
7 h-index
8 publications
331 citations
Duke University
Durham, United States
7 h-index
14 publications
437 citations

Centre National de la Recherche Scientifique
Paris, France
7 h-index
25 publications
1.1K citations
Universidade de São Paulo
São Paulo, Brazil
6 h-index
13 publications
220 citations
Université de Versailles Saint-Quentin-en-Yvelines
Versailles, France
5 h-index
17 publications
835 citations
1–8 of 344
17 publications · 644 citations
12
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6 publications · 232 citations
6
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JASA · 5 publications · 277 citations
5
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AJE · 4 publications · 538 citations
4
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ApJ · 4 publications · 1.4K citations
4
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1–5