What is he best known for?
The fields of study he is best known for:
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.
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