D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 122 Citations 362,453 342 World Ranking 10 National Ranking 8

Research.com Recognitions

Awards & Achievements

2019 - Fellow of the Royal Society, United Kingdom

2012 - Member of the National Academy of Sciences

1996 - COPSS Presidents' Award

1994 - Fellow of John Simon Guggenheim Memorial Foundation

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Cancer
  • Gene

Robert Tibshirani mostly deals with Statistics, Artificial intelligence, Data mining, Lasso and Applied mathematics. The study incorporates disciplines such as Machine learning, Regression and Pattern recognition in addition to Artificial intelligence. His biological study spans a wide range of topics, including Poisson distribution, Negative binomial distribution, Cluster analysis, False discovery rate and Data set.

His work on Elastic net regularization as part of general Lasso study is frequently linked to Shrinkage estimator, bridging the gap between disciplines. His Elastic net regularization study deals with Coordinate descent intersecting with Graphical model and Regularization. His Applied mathematics research is multidisciplinary, incorporating elements of Backfitting algorithm, Linear model, Linear regression and Generalized additive model.

His most cited work include:

  • An introduction to the bootstrap (35495 citations)
  • Regression Shrinkage and Selection via the Lasso (27684 citations)
  • The elements of statistical learning : data mining, inference,and prediction (17720 citations)

What are the main themes of his work throughout his whole career to date?

Robert Tibshirani spends much of his time researching Statistics, Artificial intelligence, Lasso, Algorithm and Machine learning. His Statistics research includes themes of Econometrics and Applied mathematics. His research on Applied mathematics often connects related topics like Generalized additive model.

As part of his studies on Artificial intelligence, Robert Tibshirani often connects relevant areas like Pattern recognition. Robert Tibshirani interconnects Data mining, Linear model, Feature selection and Regression in the investigation of issues within Lasso. His study on Linear model is mostly dedicated to connecting different topics, such as Linear regression.

He most often published in these fields:

  • Statistics (16.80%)
  • Artificial intelligence (16.64%)
  • Lasso (14.26%)

What were the highlights of his more recent work (between 2015-2021)?

  • Lasso (14.26%)
  • Artificial intelligence (16.64%)
  • Algorithm (10.46%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Lasso, Artificial intelligence, Algorithm, Internal medicine and Statistics. Robert Tibshirani specializes in Lasso, namely Elastic net regularization. Robert Tibshirani has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.

His Algorithm study combines topics from a wide range of disciplines, such as Matrix, Training set and Type I and type II errors. Robert Tibshirani works mostly in the field of Internal medicine, limiting it down to concerns involving Oncology and, occasionally, Diffuse large B-cell lymphoma. His studies in Proportional hazards model integrate themes in fields like Biobank, Logistic regression, Data mining and Survival data.

Between 2015 and 2021, his most popular works were:

  • An Introduction to Statistical Learning: with Applications in R (1700 citations)
  • Exact Post-Selection Inference for Sequential Regression Procedures (194 citations)
  • An immune clock of human pregnancy. (153 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Cancer
  • Internal medicine

Robert Tibshirani mainly investigates Internal medicine, Oncology, Lasso, Artificial intelligence and Inference. Robert Tibshirani combines subjects such as Biobank and Placebo with his study of Internal medicine. Lasso is a subfield of Statistics that Robert Tibshirani studies.

In general Statistics, his work in Elastic net regularization, Linear model, Range and Consistency is often linked to Residual sum of squares linking many areas of study. His Artificial intelligence research incorporates elements of Transparency, Machine learning, Linear regression and Data mining. His research in Inference intersects with topics in Statistical learning, Gene expression profiling, Expression, Translation and Extrapolation.

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.

Best Publications

An introduction to the bootstrap

Bradley Efron;Robert J. Tibshirani.
(1993)

45136 Citations

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

Trevor Hastie;Robert J. Tibshirani;Jerome Friedman.
The Mathematical Intelligencer (2005)

44436 Citations

Regression Shrinkage and Selection via the Lasso

Robert Tibshirani.
Journal of the royal statistical society series b-methodological (1996)

36029 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

Significance analysis of microarrays applied to the ionizing radiation response

Virginia Goss Tusher;Robert Tibshirani;Gilbert Chu.
Proceedings of the National Academy of Sciences of the United States of America (2001)

13408 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

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling

Ash A. Alizadeh;Michael B. Eisen;R. Eric Davis;Izidore S. Lossos.
Nature (2000)

10864 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

Best Scientists Citing Robert Tibshirani

Anne Lise Børresen-Dale

Anne Lise Børresen-Dale

Oslo University Hospital

Publications: 225

Charles M. Perou

Charles M. Perou

University of North Carolina at Chapel Hill

Publications: 214

Dinggang Shen

Dinggang Shen

ShanghaiTech University

Publications: 208

Jianqing Fan

Jianqing Fan

Princeton University

Publications: 163

Jorge S. Reis-Filho

Jorge S. Reis-Filho

Memorial Sloan Kettering Cancer Center

Publications: 144

Vessela N. Kristensen

Vessela N. Kristensen

Oslo University Hospital

Publications: 139

Louis M. Staudt

Louis M. Staudt

National Institutes of Health

Publications: 136

Vince D. Calhoun

Vince D. Calhoun

Georgia State University

Publications: 134

Jieping Ye

Jieping Ye

Arizona State University

Publications: 126

Georgios B. Giannakis

Georgios B. Giannakis

University of Minnesota

Publications: 125

Trevor Hastie

Trevor Hastie

Stanford University

Publications: 125

Eric P. Xing

Eric P. Xing

Carnegie Mellon University

Publications: 125

Alfred O. Hero

Alfred O. Hero

University of Michigan–Ann Arbor

Publications: 123

Randy D. Gascoyne

Randy D. Gascoyne

BC Cancer Agency

Publications: 122

Masashi Sugiyama

Masashi Sugiyama

University of Tokyo

Publications: 116

Francis Bach

Francis Bach

French Institute for Research in Computer Science and Automation - INRIA

Publications: 116

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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