Robert Tibshirani
Impact in
- Statistics and Probability top 0.01%
- Statistical Methods and Inference
- Advanced Statistical Methods and Models
- Statistical Methods and Bayesian Inference
- Artificial Intelligence top 0.01%
- Bayesian Methods and Mixture Models
Papers in
-
- Gene expression and cancer classification 71
- Bioinformatics and Genomic Networks 32
- Molecular Biology Techniques and Applications 31
-
- Statistical Methods and Inference 89
- Advanced Statistical Methods and Models 54
- Statistical Methods and Bayesian Inference 28
- Co-authors
- Trevor Hastie (90 shared papers)Bradley Efron (18 shared papers)Jerome H. Friedman (15 shared papers)J. Friedman (3 shared papers)John D. Storey (5 shared papers)Daniela Witten (21 shared papers)Gilbert Chu (5 shared papers)Gareth James (4 shared papers)
- Journals
- Journal of the American Statistical Association (31 papers)Proceedings of the National Academy of Sciences (26 papers)Blood (25 papers)The Annals of Statistics (19 papers)Journal of the Royal Statistical Society Series B (Statistical Methodology) (14 papers)
- Partner nations
- United StatesCanadaNorway
In The Last Decade
Robert Tibshirani
398 papers receiving 227.6k citations
Robert Tibshirani's Hit Papers
Peers
Comparison fields: 5 of 249
- Statistics and Probability 29.9k
- Artificial Intelligence 37.4k
- Cancer Research 16.8k
- Computational Mathematics 512
- Computer Vision and Pattern Recognition 17.5k
Countries citing papers authored by Robert Tibshirani
This map shows the geographic impact of Robert Tibshirani's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Robert Tibshirani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Tibshirani more than expected).
Fields of papers citing papers by Robert Tibshirani
This network shows the impact of papers produced by Robert Tibshirani. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Robert Tibshirani. The network helps show where Robert Tibshirani may publish in the future.
Co-authors
The 25 scholars most cited alongside Robert Tibshirani, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 402 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Regression Shrinkage and Selection Via the Lasso Hit paper breakdown → | 1996 | 32381 |
| 2 | An Introduction to the Bootstrap Hit paper breakdown → | 1994 | 29784 |
| 3 | The Elements of Statistical Learning Hit paper breakdown → | 2001 | 16782 |
| 4 | The Elements of Statistical Learning Hit paper breakdown → | 2009 | 14518 |
| 5 | Regularization Paths for Generalized Linear Models via Coordinate Descent Hit paper breakdown → | 2010 | 10834 |
| 6 | The Elements of Statistical Learning: Data Mining, Inference, and Prediction Hit paper breakdown → | 2013 | 9165 |
| 7 | Significance analysis of microarrays applied to the ionizing radiation response Hit paper breakdown → | 2001 | 8907 |
| 8 | Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications Hit paper breakdown → | 2001 | 8255 |
| 9 | An Introduction to Statistical Learning Hit paper breakdown → | 2013 | 7797 |
| 10 | Statistical significance for genomewide studies Hit paper breakdown → | 2003 | 7345 |
| 11 | Generalized Additive Models. Hit paper breakdown → | 1991 | 6752 |
| 12 | Least angle regression Hit paper breakdown → | 2004 | 6102 |
| 13 | Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors) Hit paper breakdown → | 2000 | 4806 |
| 14 | Repeated observation of breast tumor subtypes in independent gene expression data sets Hit paper breakdown → | 2003 | 4034 |
| 15 | THE LASSO METHOD FOR VARIABLE SELECTION IN THE COX MODEL Hit paper breakdown → | 1997 | 2873 |
| 16 | Missing value estimation methods for DNA microarrays Hit paper breakdown → | 2001 | 2794 |
| 17 | Regression Shrinkage and Selection via The Lasso: A Retrospective Hit paper breakdown → | 2011 | 2542 |
| 18 | Generalized Additive Models Hit paper breakdown → | 1986 | 2506 |
| 19 | Diagnosis of multiple cancer types by shrunken centroids of gene expression Hit paper breakdown → | 2002 | 2082 |
| 20 | Sparse Principal Component Analysis Hit paper breakdown → | 2006 | 1987 |
About Robert Tibshirani
Robert Tibshirani is a scholar working on Molecular Biology, Statistics and Probability, Artificial Intelligence, Cancer Research and Pathology and Forensic Medicine, having authored 402 papers that have together received 238.4k indexed citations. Recurring topics across this work include Statistical Methods and Inference (89 papers), Gene expression and cancer classification (71 papers), Advanced Statistical Methods and Models (54 papers), Bioinformatics and Genomic Networks (32 papers), Molecular Biology Techniques and Applications (31 papers), Statistical Methods and Bayesian Inference (28 papers), Lymphoma Diagnosis and Treatment (25 papers) and Face and Expression Recognition (18 papers). The work is most often cited by research in Statistics and Probability (29.9k citations), Artificial Intelligence (37.4k citations), Cancer Research (16.8k citations), Computational Mathematics (512 citations) and Computer Vision and Pattern Recognition (17.5k citations). Robert Tibshirani has collaborated with scholars based in United States, Canada and Norway. Frequent co-authors include Trevor Hastie, Bradley Efron, Jerome H. Friedman, J. Friedman, John D. Storey, Daniela Witten, Gilbert Chu, Gareth James, Richard A. Brown and Iain M. Johnstone. Their work appears in journals such as Journal of the American Statistical Association, Proceedings of the National Academy of Sciences, Blood, The Annals of Statistics and Journal of the Royal Statistical Society Series B (Statistical Methodology).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.