Jonathan E. Taylor
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North American statistician working with neuroimaging
Jonathan E. Taylor's AcademicInfluence.com Rankings
Jonathan E. Taylormathematics Degrees
Mathematics
#8058
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#10942
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Control Theory
#65
World Rank
#69
Historical Rank
Statistics
#1094
World Rank
#1197
Historical Rank
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Mathematics
Jonathan E. Taylor's Degrees
- PhD Statistics Stanford University
- Masters Applied Mathematics University of California, Berkeley
- Bachelors Mathematics University of California, Berkeley
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(Suggest an Edit or Addition)Jonathan E. Taylor's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach (2004) (1410)
- Distributed Neural Representation of Expected Value (2005) (950)
- The solution path of the generalized lasso (2010) (767)
- A SIGNIFICANCE TEST FOR THE LASSO. (2013) (637)
- Exact post-selection inference, with application to the lasso (2013) (602)
- Strong rules for discarding predictors in lasso‐type problems (2010) (536)
- A LASSO FOR HIERARCHICAL INTERACTIONS. (2012) (430)
- Exact Post-Selection Inference for Sequential Regression Procedures (2014) (381)
- Unified univariate and multivariate random field theory (2004) (369)
- Degrees of freedom in lasso problems (2011) (341)
- Optimal Inference After Model Selection (2014) (284)
- Statistical learning and selective inference (2015) (272)
- Forward stagewise regression and the monotone lasso (2007) (221)
- Interpretable whole-brain prediction analysis with GraphNet (2013) (199)
- Statistical mapping analysis of lesion location and neurological disability in multiple sclerosis: application to 452 patient data sets (2003) (188)
- Detecting Sparse Signals in Random Fields, With an Application to Brain Mapping (2007) (140)
- Post‐selection inference for ℓ1 ‐penalized likelihood models (2016) (139)
- Communication-efficient Sparse Regression (2017) (133)
- Selective inference with a randomized response (2015) (118)
- Cross‐subject comparison of principal diffusion direction maps (2005) (115)
- A Generalized Least-Square Matrix Decomposition (2014) (112)
- Diffusion smoothing on brain surface via finite element method (2004) (95)
- Exact Post Model Selection Inference for Marginal Screening (2014) (92)
- Selecting the number of principal components: estimation of the true rank of a noisy matrix (2014) (89)
- High-dimensional regression adjustments in randomized experiments (2016) (87)
- A tail strength measure for assessing the overall univariate significance in a dataset. (2005) (86)
- Exact post-selection inference with the lasso (2013) (83)
- FALSE DISCOVERY RATE ANALYSIS OF BRAIN DIFFUSION DIRECTION MAPS. (2008) (81)
- The 'miss rate' for the analysis of gene expression data. (2005) (74)
- Post-selection adaptive inference for Least Angle Regression and the Lasso (2014) (71)
- A gaussian kinematic formula (2006) (68)
- Why adaptively collected data have negative bias and how to correct for it (2017) (68)
- Empirical null and false discovery rate analysis in neuroimaging (2009) (65)
- Communication-efficient sparse regression: a one-shot approach (2015) (60)
- Asymptotics of Selective Inference (2015) (59)
- Inference for eigenvalues and eigenvectors of Gaussian symmetric matrices (2008) (56)
- Topological Consistency via Kernel Estimation (2014) (56)
- Selective Sequential Model Selection (2015) (54)
- A significance test for forward stepwise model selection (2014) (51)
- Diffusion smoothing on the cortical surface (2001) (48)
- Exact Post-selection Inference for Forward Stepwise and Least Angle Regression (2014) (48)
- On model selection consistency of regularized M-estimators (2013) (47)
- Selective inference in regression models with groups of variables (2015) (39)
- Selective sampling after solving a convex problem (2016) (39)
- Tests in adaptive regression via the Kac-Rice formula (2013) (37)
- Maxima of discretely sampled random fields, with an application to 'bubbles' (2005) (36)
- Post‐selection point and interval estimation of signal sizes in Gaussian samples (2014) (35)
- Selective inference with unknown variance via the square-root lasso (2015) (30)
- Detecting fMRI activation allowing for unknown latency of the hemodynamic response (2006) (30)
- Bayesian Post-Selection Inference in the Linear Model (2016) (29)
- Group Comparison of Eigenvalues and Eigenvectors of Diffusion Tensors (2010) (29)
- A Generalized Least Squares Matrix Decomposition (2011) (28)
- A statistician plays darts (2011) (25)
- Adaptive testing for the graphical lasso (2013) (23)
- A General Framework for Estimation and Inference From Clusters of Features (2015) (20)
- Exact inference after model selection via the Lasso (2013) (20)
- On model selection consistency of M-estimators with geometrically decomposable penalties (2013) (19)
- MAGIC: a general, powerful and tractable method for selective inference (2016) (18)
- A family of interpretable multivariate models for regression and classification of whole-brain fMRI data (2011) (18)
- Inference in adaptive regression via the Kac–Rice formula (2016) (18)
- Special Issue on Mathematics in Brain Imaging (2009) (15)
- Bootstrap inference after using multiple queries for model selection (2016) (15)
- Integrative methods for post-selection inference under convex constraints (2019) (14)
- On model selection consistency of penalized M-estimators: a geometric theory (2013) (13)
- Unifying approach to selective inference with applications to cross-validation (2017) (12)
- Approximate selective inference via maximum likelihood (2019) (12)
- Scalable methods for Bayesian selective inference (2017) (12)
- Inferactive data analysis (2017) (12)
- An MCMC-free approach to post-selective inference (2017) (10)
- Evaluating the statistical significance of biclusters (2015) (9)
- Rejoinder: "A significance test for the lasso" (2014) (9)
- Selection Corrected Statistical Inference for Region Detection with High-throughput Assays (2016) (9)
- Whole-brain Prediction Analysis with GraphNet (2011) (7)
- Acknowledgement of priority: Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach (2005) (6)
- A Selective Approach to Internal Inference (2015) (6)
- Comparison of prediction errors: Adaptive p-values after cross-validation (2017) (6)
- Regularization Paths for Least Squares Problems with Generalized $\ell_1$ Penalties (2010) (6)
- Supplement : Proofs and Technical Details for “ The Solution Path of the Generalized Lasso ” (2013) (5)
- Detecting sparse cone alternatives for Gaussian random fields, with an application to fMRI (2012) (5)
- Correction to rejoinder to “A significance test for the Lasso” (2014) (4)
- Inference After Selecting Plausibly Valid Instruments with Application to Mendelian Randomization (2019) (4)
- Adaptive p-values after cross-validation (2017) (4)
- Sparse Steinian Covariance Estimation (2017) (3)
- Survival analysis on rare events using group-regularized multi-response Cox regression (2020) (3)
- Inferring Treatment Effects After Testing Instrument Strength in Linear Models. (2020) (3)
- Inference after black box selection. (2019) (2)
- Thresholding non-stationary SPMs with an application to cortical surface mapping (2001) (2)
- A Tribute to: Keith Worsley — 1951–2009 (2009) (2)
- Correlation between MS lesions and disability using 3D voxel-based statistical analysis (2000) (1)
- Discussion: " a Significance Test for the Lasso " (2014) (1)
- A tail strength measure for assessing the overall significance in a dataset (2005) (1)
- Pliable Methods for Post-Selection Inference Under Convex Constraints (2016) (1)
- Black-box Selective Inference via Bootstrapping (2022) (1)
- Spatio-Temporal Dimension Reduction via Sparse Generalized PCA (2011) (1)
- Valid post-correction inference for censored regression problems (2014) (1)
- A SELECTIVE SURVEY OF SELECTIVE INFERENCE (2019) (1)
- A SIGNIFICANCE TEST FOR THE LASSO1 (2014) (1)
- Sampling from a pseudo selective posterior using a primal-dual approach (2017) (0)
- univariate signicance in a dataset (2005) (0)
- 1-30-2012 A significance test for the lasso (2015) (0)
- Stephen Lisberger What do you expect ? : fMRI of Expected Utility (2004) (0)
- Lasso Degrees of Freedom when p > n (2011) (0)
- Rejoinder (2016) (0)
- Simultaneous Coverage : It also makes sense to talk about events that are defined simultaneously over all j ∈ (2015) (0)
- Exact Selective Inference with Randomization (2022) (0)
- THE LASSO AND RELATED METHODS 2 1 The lasso and related methods (2007) (0)
- Supplement to “ A Generalized Least Squares Matrix Decomposition ” (2013) (0)
- Fast Sparse-Group Lasso Method for Multi-response Cox Model with Applications to UK Biobank (2020) (0)
- The University of Chicago Department of Statistics Seminar Series (2011) (0)
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