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- Trevor Hastie

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
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Citations
Publications
World Ranking
National Ranking

Engineering and Technology
D-index
92
Citations
216,043
198
World Ranking
49
National Ranking
27

2019 - Royal Netherlands Academy of Arts and Sciences

2019 - Wald Memorial Lecturer

2018 - Member of the National Academy of Sciences

1998 - Fellow of the American Statistical Association (ASA)

- Statistics
- Artificial intelligence
- Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Statistics, Lasso, Applied mathematics and Pattern recognition. His Artificial intelligence research integrates issues from Machine learning and Regression. His work on Regression analysis, Nonparametric regression and Model selection as part of his general Statistics study is frequently connected to Band matrix, thereby bridging the divide between different branches of science.

His specific area of interest is Lasso, where Trevor Hastie studies Elastic net regularization. His Applied mathematics study combines topics in areas such as Backfitting algorithm, Proportional hazards model, Symmetric matrix and Generalized additive model. His research integrates issues of Species richness and Generalized additive model for location, scale and shape in his study of Generalized additive model.

- The elements of statistical learning : data mining, inference,and prediction (17720 citations)
- The Elements of Statistical Learning (15816 citations)
- Regularization and variable selection via the elastic net (10840 citations)

His primary areas of study are Artificial intelligence, Statistics, Algorithm, Machine learning and Pattern recognition. His research related to Linear discriminant analysis, Support vector machine, Supervised learning, Boosting and Classifier might be considered part of Artificial intelligence. His study in Econometrics extends to Statistics with its themes.

His work carried out in the field of Algorithm brings together such families of science as Matrix and Lasso. Trevor Hastie works mostly in the field of Lasso, limiting it down to topics relating to Applied mathematics and, in certain cases, Linear regression, as a part of the same area of interest. His Feature selection research is mostly focused on the topic Elastic net regularization.

- Artificial intelligence (27.67%)
- Statistics (16.51%)
- Algorithm (14.65%)

- Artificial intelligence (27.67%)
- Biobank (3.02%)
- Lasso (11.63%)

Trevor Hastie focuses on Artificial intelligence, Biobank, Lasso, Algorithm and Machine learning. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Natural language processing and Pattern recognition. His research in Lasso is mostly focused on Elastic net regularization.

His Elastic net regularization research is multidisciplinary, incorporating elements of Supervised learning, Logistic regression and Generalized linear model, Applied mathematics. His work in Algorithm addresses subjects such as Random forest, which are connected to disciplines such as Synthetic data. Many of his research projects under Machine learning are closely connected to Prognostic model with Prognostic model, tying the diverse disciplines of science together.

- An Introduction to Statistical Learning: with Applications in R (1700 citations)
- Surprises in High-Dimensional Ridgeless Least Squares Interpolation. (183 citations)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd Edition (133 citations)

- Statistics
- Artificial intelligence
- Machine learning

Trevor Hastie mainly focuses on Artificial intelligence, Lasso, Feature selection, Disease and Biobank. His work deals with themes such as Machine learning and Natural language processing, which intersect with Artificial intelligence. Trevor Hastie studies Lasso, namely Elastic net regularization.

His Elastic net regularization study combines topics from a wide range of disciplines, such as Generalized linear model and Applied mathematics. His Feature selection study integrates concerns from other disciplines, such as Algorithm and Solver. His work in Algorithm tackles topics such as Estimator which are related to areas like Regression analysis.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

The elements of statistical learning : data mining, inference,and prediction

Trevor Hastie;Robert J. Tibshirani;Jerome Friedman.

The Mathematical Intelligencer **(2005)**

44436 Citations

Generalized Additive Models

Trevor J. Hastie;Robert Tibshirani.

**(1990)**

18604 Citations

The Elements of Statistical Learning

Trevor Hastie;Robert Tibshirani;Jerome H. Friedman.

**(2001)**

14349 Citations

Regularization and variable selection via the elastic net

Hui Zou;Trevor Hastie.

Journal of The Royal Statistical Society Series B-statistical Methodology **(2005)**

13975 Citations

Generalized Additive Models.

R. A. Brown;T. J. Hastie;R. J. Tibshirani.

Biometrics **(1991)**

12808 Citations

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

Therese Sørlie;Charles M. Perou;Robert Tibshirani;Turid Aas.

Proceedings of the National Academy of Sciences of the United States of America **(2001)**

11924 Citations

Least angle regression

Bradley Efron;Trevor Hastie;Iain Johnstone;Robert Tibshirani.

Annals of Statistics **(2004)**

9815 Citations

Additive Logistic Regression : A Statistical View of Boosting

Jerome Friedman;Trevor Hastie;Robert Tibshirani.

Annals of Statistics **(2000)**

9308 Citations

Regularization Paths for Generalized Linear Models via Coordinate Descent

Jerome Friedman;Trevor Hastie;Robert Tibshirani.

Journal of Statistical Software **(2010)**

9108 Citations

An introduction to statistical learning

Gareth James;Daniela Witten;Trevor Hastie;Robert Tibshirani.

**(2013)**

8319 Citations

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Profile was last updated on December 6th, 2021.

Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).

The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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