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D-Index & Metrics

Mathematics

D-Index
43
Citations
11676
World Ranking
1658
National Ranking
715

Overview

Ryan J. Tibshirani is affiliated with the University of California, Berkeley in the United States. Their research spans multiple disciplines including mathematics, computer science, and medicine, with a substantial focus on statistics and probability, artificial intelligence, and epidemiology.

Their primary research topics include data-driven disease surveillance, COVID-19 epidemiological studies, statistical methods and inference, advanced statistical methods and models, influenza virus research studies, machine learning and data classification, and anomaly detection techniques and applications.

Frequent collaborators in their research include Alden Green, Roni Rosenfeld, Addison J. Hu, Daniel J. McDonald, and Natalia L. Oliveira. These partnerships have contributed to a growing body of work published in a variety of venues.

Ryan J. Tibshirani frequently publishes in the following venues:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • The Annals of Statistics
  • Proceedings of the National Academy of Sciences
  • Statistical Science

Selected recent publications include:

  • Surprises in high-dimensional ridgeless least squares interpolation, 2022, The Annals of Statistics
  • Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons, 2020, Statistical Science
  • The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination, 2021, Proceedings of the National Academy of Sciences
  • Conformal prediction beyond exchangeability, 2023, The Annals of Statistics
  • Collaborative Hubs: Making the Most of Predictive Epidemic Modeling, 2022, American Journal of Public Health

Best Publications

  • The solution path of the generalized lasso

    Ryan J. Tibshirani;Jonathan Taylor

  • A SIGNIFICANCE TEST FOR THE LASSO.

    Richard Lockhart;Jonathan Taylor;Ryan J. Tibshirani;Robert Tibshirani

  • Strong rules for discarding predictors in lasso-type problems

    Robert Tibshirani;Jacob Bien;Jerome Friedman;Trevor Hastie

  • Distribution-Free Predictive Inference for Regression

    Jing Lei;Max G'Sell;Alessandro Rinaldo;Ryan J. Tibshirani

  • The Lasso Problem and Uniqueness

    Ryan J. Tibshirani

  • Surprises in High-Dimensional Ridgeless Least Squares Interpolation.

    Trevor Hastie;Andrea Montanari;Saharon Rosset;Ryan J. Tibshirani

  • Adaptive piecewise polynomial estimation via trend filtering

    Ryan J. Tibshirani

  • Degrees of freedom in lasso problems

    Ryan J. Tibshirani;Jonathan Taylor

  • Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

    Unknown

  • Exact Post-Selection Inference for Sequential Regression Procedures

    Ryan J. Tibshirani;Jonathan Taylor;Richard Lockhart;Robert Tibshirani

  • Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons

    Unknown

  • Trend filtering on graphs

    Yu-Xiang Wang;James Sharpnack;Alexander J. Smola;Ryan J. Tibshirani

  • The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

    Unknown

  • An open challenge to advance probabilistic forecasting for dengue epidemics.

    Michael A. Johansson;Michael A. Johansson;Karyn M. Apfeldorf;Scott Dobson;Jason Devita

  • Predictive inference with the jackknife

    Rina Foygel Barber;Emmanuel J. Candès;Aaditya Ramdas;Ryan J. Tibshirani

  • Conformal prediction beyond exchangeability

    Unknown

  • Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso

    Trevor Hastie;Robert Tibshirani;Ryan J. Tibshirani

  • The United States COVID-19 Forecast Hub dataset

    Unknown

  • Nearly-Isotonic Regression

    Ryan J. Tibshirani;Holger Hoefling;Robert Tibshirani

  • Flexible Modeling of Epidemics with an Empirical Bayes Framework

    Logan C. Brooks;David C. Farrow;Sangwon Hyun;Ryan J. Tibshirani

  • Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016

    Craig J. McGowan;Matthew Biggerstaff;Michael Johansson;Karyn M. Apfeldorf

  • A bias correction for the minimum error rate in cross-validation

    Ryan J. Tibshirani;Robert Tibshirani

  • The limits of distribution-free conditional predictive inference

    Rina Foygel Barber;Emmanuel J Candès;Aaditya Ramdas;Ryan J Tibshirani

  • Efficient Implementations of the Generalized Lasso Dual Path Algorithm

    Taylor B. Arnold;Ryan J. Tibshirani

  • Conformal Prediction Under Covariate Shift

    Ryan J. Tibshirani;Rina Foygel Barber;Emmanuel J. Candès;Aaditya Ramdas

Frequent Co-Authors

Robert Tibshirani
Robert Tibshirani Stanford University
Jonathan Taylor
Jonathan Taylor Stanford University
Roni Rosenfeld
Roni Rosenfeld Carnegie Mellon University
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Alessandro Rinaldo
Alessandro Rinaldo The University of Texas at Austin
Emmanuel J. Candès
Emmanuel J. Candès Stanford University
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Saharon Rosset
Saharon Rosset Tel Aviv University
Trevor Hastie
Trevor Hastie Stanford University
J. Zico Kolter
J. Zico Kolter Carnegie Mellon University

External Links

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