R
Robert Tibshirani
Researcher at Stanford University
Publications - 620
Citations - 359457
Robert Tibshirani is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Gene expression profiling. The author has an hindex of 147, co-authored 593 publications receiving 326580 citations. Previous affiliations of Robert Tibshirani include University of Toronto & University of California.
Papers
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Journal ArticleDOI
Missing value estimation methods for DNA microarrays.
Olga G. Troyanskaya,Michael N. Cantor,Gavin Sherlock,Patrick O. Brown,Trevor Hastie,Robert Tibshirani,David Botstein,Russ B. Altman +7 more
TL;DR: It is shown that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVD Impute and KNN Impute surpass the commonly used row average method (as well as filling missing values with zeros).
Book
An Introduction to Statistical Learning: with Applications in R
TL;DR: This book presents some of the most important modeling and prediction techniques, along with relevant applications, that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
Journal ArticleDOI
Sparse Principal Component Analysis
TL;DR: This work introduces a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified principal components with sparse loadings and shows that PCA can be formulated as a regression-type optimization problem.
Journal ArticleDOI
Regression shrinkage and selection via the lasso: a retrospective
TL;DR: In this article, the authors give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.
Journal ArticleDOI
The lasso method for variable selection in the cox model
TL;DR: Simulations indicate that the lasso can be more accurate than stepwise selection in this setting and reduce the estimation variance while providing an interpretable final model in Cox's proportional hazards model.