Surrogate Variable Models
Surrogate Variable Models is a statistical modeling framework within Bayesian measurement error models that uses auxiliary variables as proxies for unobserved true predictors, distinguishing itself by enabling valid inference in the presence of measurement error through explicit modeling of the relationship between observed surrogates and latent variables.
67
Publications
9.7K
Citations
253
Authors
131
Institutions
Publications per year
2017–2026
20
Variants
Surrogate Variable Model
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131

Johns Hopkins University
Baltimore, United States
6 h-index
20 publications
2.3K citations
University of Michigan
Ann Arbor, United States
3 h-index
7 publications
369 citations
University of Washington
Seattle, United States
3 h-index
4 publications
4.7K citations
Harvard University
Cambridge, United States
3 h-index
5 publications
296 citations

The University of Melbourne
Melbourne, Australia
2 h-index
7 publications
541 citations
University of Miami
Coral Gables, United States
2 h-index
2 publications
121 citations
University of Florida
Gainesville, United States
2 h-index
2 publications
67 citations
Cancer Research And Biostatistics
Seattle, United States
2 h-index
2 publications
115 citations
1–8 of 131
3 publications · 2.1K citations
3
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3 publications · 544 citations
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3 publications · 143 citations
3
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2 publications · 545 citations
2
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2 publications · 144 citations
2
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