Trevor Hastie: h-index, Total Citations, and Citation Map
Trevor Hastie's h-index is 166 (457 i10-index, 447,441+ total citations across 5+ publications) according to Google Scholar as of June 2026. Trevor Hastie is affiliated with Professor of Statistics, Stanford University.
Trevor Hastie is a researcher affiliated with Professor of Statistics, Stanford University, specializing in Statistical learning and modeling, data mining, machine learning. Their work has been cited 447,441 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Trevor Hastie's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 166
- i10-Index
- 457
- Total Citations
- 447,441
- Citing Countries
- 18
As of June 2026.
Trevor Hastie has an h-index of 166 and 447,441 total citations across 5 publications, with research cited by institutions in 18 countries.
Global Impact Map
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
2009109,013
Top Citing Countries
Top Citing Institutions
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 established a foundational framework for statistical learning, data mining, and prediction through a seminal textbook that has become a standard reference in the field.
The researcher authored a seminal textbook on statistical learning that has become a foundational reference for data mining, inference, and prediction, accumulating over 100,000 citations.
The researcher developed the elastic net, a regularization method combining L1 and L2 penalties to address variable selection challenges in high-dimensional statistical modeling.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Trevor Hastie's research
Trevor Hastie is a researcher in Statistical learning and modeling, data mining and machine learning at Professor of Statistics, Stanford University. Their work has been cited 447,441 times across 5 publications (h-index 166), according to Google Scholar.
Their most-cited work, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction” (2009), has accumulated 109,013 citations. Other influential works include “The Elements of Statistical Learning: Data Mining, Inference, and Prediction” (2001) with 102,391 citations and “Introduction to Statistical Learning” (2013) with 33,744 citations.
Citations of Trevor Hastie's research come primarily from United States, United Kingdom and China, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
