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D-Index & Metrics

Mathematics

D-Index
50
Citations
18038
World Ranking
1053
National Ranking
489

Overview

Jonathan Taylor is affiliated with Stanford University in the United States. Their research centers on fields of mathematics, with specific emphasis on statistics and probability. Their work also extends into subfields including molecular biology, artificial intelligence, environmental engineering, and management science and operations research.

Their scholarly output includes contributions to several main topics, such as statistical methods and inference, Gaussian processes and Bayesian inference, advanced causal inference techniques, statistical methods and Bayesian inference more broadly, ferroptosis and cancer prognosis, bioinformatics and genomic networks, and soil geostatistics and mapping.

Jonathan Taylor has published frequently in venues such as arXiv (Cornell University), with four publications, the Journal of the American Statistical Association, with two publications, bioRxiv (Cold Spring Harbor Laboratory), with two publications, The Annals of Statistics, and Science Advances.

Recent papers authored by or involving Jonathan Taylor include:

  • Approximate Selective Inference via Maximum Likelihood, 2022, Journal of the American Statistical Association
  • Integrative methods for post-selection inference under convex constraints, 2021, The Annals of Statistics
  • Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI, 2022, Science Advances
  • Survival analysis on rare events using group-regularized multi-response Cox regression, 2021, Bioinformatics
  • Inferring Treatment Effects After Testing Instrument Strength in Linear Models, 2020, arXiv (Cornell University)

Jonathan Taylor has collaborated frequently with other researchers, including Robert Tibshirani, Trevor Hastie, Gareth James, Daniela Witten, and Snigdha Panigrahi.

In addition to journal articles, Jonathan Taylor has contributed to book publications. One noted publication is An Introduction to Statistical Learning, published by Springer International Publishing in 2023, which has accumulated a notable number of citations.

Best Publications

  • Random Fields and Geometry

    Robert J. Adler;Jonathan E. Taylor

  • Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach

    John D. Storey;Jonathan E. Taylor;David Siegmund

  • Distributed neural representation of expected value.

    Brian Knutson;Jonathan Taylor;Matthew Kaufman;Richard Peterson

  • The solution path of the generalized lasso

    Ryan J. Tibshirani;Jonathan Taylor

  • Exact post-selection inference, with application to the lasso

    Jason D. Lee;Dennis L. Sun;Yuekai Sun;Jonathan E. 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

  • A lasso for hierarchical interactions

    Jacob Bien;Jonathan Taylor;Robert Tibshirani

  • Structural Asymmetries in the Human Brain: a Voxel-based Statistical Analysis of 142 MRI Scans

    K E Watkins;T Paus;J P Lerch;A Zijdenbos

  • Degrees of freedom in lasso problems

    Ryan J. Tibshirani;Jonathan Taylor

  • Unified univariate and multivariate random field theory.

    Keith J. Worsley;Jonathan E. Taylor;Francesco Tomaiuolo;Jason Lerch

  • Statistical learning and selective inference

    Jonathan Taylor;Robert J. Tibshirani

  • Exact Post-Selection Inference for Sequential Regression Procedures

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

  • Genotypic predictors of human immunodeficiency virus type 1 drug resistance

    Soo-Yon Rhee;Jonathan Taylor;Gauhar Wadhera;Asa Ben-Hur

  • Deformation-based surface morphometry applied to gray matter deformation.

    Moo K. Chung;Keith J. Worsley;Keith J. Worsley;Steve Robbins;Tomáš Paus

  • Optimal Inference After Model Selection

    William Fithian;Dennis Sun;Jonathan Taylor

  • Forward Stagewise Regression and the Monotone Lasso

    Trevor Hastie;Jonathan Taylor;Robert Tibshirani;Guenther Walther

  • SurfStat: A Matlab toolbox for the statistical analysis of univariate and multivariate surface and volumetric data using linear mixed effects models and random field theory

    KJ Worsley;JE Taylor;F Carbonell;MK Chung

  • Statistical mapping analysis of lesion location and neurological disability in multiple sclerosis: application to 452 patient data sets

    Arnaud Charil;Alex P Zijdenbos;Jonathan Taylor;Cyrus Boelman

  • Interpretable whole-brain prediction analysis with GraphNet

    Logan Grosenick;Brad Klingenberg;Kiefer Katovich;Brian Knutson

Frequent Co-Authors

Robert Tibshirani
Robert Tibshirani Stanford University
Robert J. Adler
Robert J. Adler Technion – Israel Institute of Technology
Ryan J. Tibshirani
Ryan J. Tibshirani University of California, Berkeley
Keith J. Worsley
Keith J. Worsley McGill University
Jason D. Lee
Jason D. Lee Princeton University
Robert W. Shafer
Robert W. Shafer Stanford University
Soo-Yon Rhee
Soo-Yon Rhee Stanford University
Moo K. Chung
Moo K. Chung University of Wisconsin–Madison
Alan C. Evans
Alan C. Evans McGill University
Brian Knutson
Brian Knutson Stanford University

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