James G. Scott: h-index, Total Citations, and Citation Map
James G. Scott's h-index is 43 (88 i10-index, 12,253+ total citations across 3+ publications) according to Google Scholar as of May 2026. James G. Scott is affiliated with Professor and Chair, Statistics and Data Science, University of Texas at Austin.
James G. Scott is a researcher affiliated with Professor and Chair, Statistics and Data Science, University of Texas at Austin, specializing in statistics and data science. Their work has been cited 12,253 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
James G. Scott's Citation Metrics
Bibliometric impact based on 3 indexed publications.
- H-Index
- 43
- i10-Index
- 88
- Total Citations
- 12,253
- Citing Countries
- 15
As of May 2026.
James G. Scott has an h-index of 43 and 12,253 total citations across 3 publications, with research cited by institutions in 15 countries.
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Global Impact Map
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Top Cited Works
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The horseshoe estimator for sparse signals
20102,035
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed the horseshoe estimator for sparse signals, a seminal method published in Biometrika that has garnered over 2,000 citations.
The researcher developed the Horseshoe prior to handle sparsity in high-dimensional statistical models, establishing a foundational method for robust variable selection and shrinkage estimation.
The researcher developed a Bayesian inference framework for logistic models using Pólya–Gamma latent variables, establishing a highly cited methodological standard in statistical computing.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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