Jonathan E. Taylor

发表

The solution path of the generalized lasso

R. Tibshirani, Jonathan E. Taylor, 2010, 1005.1971.

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

John D. Storey, D. Siegmund, Jonathan E. Taylor, 2004 .

Optimal Inference After Model Selection

Dennis L. Sun, Jonathan E. Taylor, William Fithian, 2014, 1410.2597.

Exact Post-selection Inference for Forward Stepwise and Least Angle Regression

R. Tibshirani, R. Tibshirani, Jonathan E. Taylor, 2014 .

A SIGNIFICANCE TEST FOR THE LASSO.

R. Tibshirani, R. Tibshirani, Jonathan E. Taylor, 2013, Annals of statistics.

Strong rules for discarding predictors in lasso‐type problems

R. Tibshirani, T. Hastie, J. Friedman, 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.

Selective Sequential Model Selection

R. Tibshirani, R. Tibshirani, Jonathan E. Taylor, 2015, 1512.02565.

Forward stagewise regression and the monotone lasso

R. Tibshirani, T. Hastie, Jonathan E. Taylor, 2007, 0705.0269.

On model selection consistency of M-estimators with geometrically decomposable penalties

Jonathan E. Taylor, J. Lee, Yuekai Sun, 2013, NIPS 2013.

Communication-efficient Sparse Regression

Qiang Liu, Jonathan E. Taylor, Jason D. Lee, 2017, J. Mach. Learn. Res..

Degrees of freedom in lasso problems

R. Tibshirani, Jonathan E. Taylor, 2011, 1111.0653.

A LASSO FOR HIERARCHICAL INTERACTIONS.

R. Tibshirani, Jonathan E. Taylor, J. Bien, 2012, Annals of statistics.

Topological Consistency via Kernel Estimation

S. Mukherjee, Jonathan E. Taylor, O. Bobrowski, 2014, 1407.5272.

Fast Sparse-Group Lasso Method for Multi-response Cox Model with Applications to UK Biobank

Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2020, bioRxiv.

Scalable methods for Bayesian selective inference

Jonathan E. Taylor, Snigdha Panigrahi, 2017, 1703.06176.

Exact post-selection inference, with application to the lasso

Dennis L. Sun, Jonathan E. Taylor, J. Lee, 2013, 1311.6238.

Exact post-selection inference with the lasso

Dennis L. Sun, Jonathan E. Taylor, J. Lee, 2013 .

A tail strength measure for assessing the overall univariate significance in a dataset.

R. Tibshirani, Jonathan E. Taylor, 2005, Biostatistics.

Inference in adaptive regression via the Kac–Rice formula

Joshua R. Loftus, R. Tibshirani, Jonathan E. Taylor, 2016 .

Inference in adaptive regression via the Kac–Rice formula

Joshua R. Loftus, R. Tibshirani, Jonathan E. Taylor, 2013, 1308.3020.

Adaptive testing for the graphical lasso

R. Tibshirani, Jonathan E. Taylor, Max G'sell, 2013, 1307.4765.

A significance test for forward stepwise model selection

Joshua R. Loftus, Jonathan E. Taylor, 2014, 1405.3920.

Selecting the number of principal components: estimation of the true rank of a noisy matrix

R. Tibshirani, Jonathan E. Taylor, Yunjin Choi, 2014, 1410.8260.

Exact inference after model selection via the Lasso

Dennis L. Sun, Jonathan E. Taylor, J. Lee, 2013 .

Adaptive p-values after cross-validation

Jonathan E. Taylor, J. Marković, Lucy Xia, 2017 .

Comparison of prediction errors: Adaptive p-values after cross-validation

Jonathan E. Taylor, J. Marković, Lucy Xia, 2017 .

Inference After Selecting Plausibly Valid Instruments with Application to Mendelian Randomization

Jonathan E. Taylor, Nan Bi, Hyunseung Kang, 2019, 1911.03985.

MAGIC: a general, powerful and tractable method for selective inference

Jonathan E. Taylor, Nan Bi, Xiaoying Tian, 2016, 1607.02630.

Post‐selection inference for ℓ1 ‐penalized likelihood models

Robert Tibshirani, Jonathan Taylor, R. Tibshirani, 2016, The Canadian journal of statistics = Revue canadienne de statistique.

Selective inference in regression models with groups of variables

Joshua R. Loftus, Jonathan E. Taylor, 2015, 1511.01478.

Selective inference with a randomized response

Jonathan E. Taylor, Xiaoying Tian, Xiaoying Tian, 2015, 1507.06739.

Rejoinder: "A significance test for the lasso"

R. Tibshirani, R. Tibshirani, Jonathan E. Taylor, 2014, 1405.6805.

Post‐selection point and interval estimation of signal sizes in Gaussian samples

Robert Tibshirani, Jonathan Taylor, R. Tibshirani, 2014, 1405.3340.

A Selective Approach to Internal Inference

Samuel M. Gross, R. Tibshirani, Jonathan E. Taylor, 2015, 1510.00486.

Unifying approach to selective inference with applications to cross-validation

Jonathan E. Taylor, J. Marković, Lucy Xia, 2017, 1703.06559.

univariate signicance in a dataset

R. Tibshirani, Jonathan E. Taylor, 2005 .

Bootstrap inference after using multiple queries for model selection

Jonathan E. Taylor, J. Marković, 2016, 1612.07811.

Discussion: " a Significance Test for the Lasso "

R. Tibshirani, Jonathan E. Taylor, R. Lockhart, 2014 .

Selective inference with unknown variance via the square-root lasso

Joshua R. Loftus, Jonathan E. Taylor, Xiaoying Tian, 2015, Biometrika.

Simultaneous Coverage : It also makes sense to talk about events that are defined simultaneously over all j ∈

Dennis L. Sun, Jonathan E. Taylor, J. Lee, 2015 .

THE LASSO AND RELATED METHODS 2 1 The lasso and related methods

R. Tibshirani, T. Hastie, Jonathan E. Taylor, 2007 .

Exact Post-Selection Inference for Sequential Regression Procedures

R. Tibshirani, R. Tibshirani, Jonathan E. Taylor, 2014, 1401.3889.

An MCMC-free approach to post-selective inference

Jonathan E. Taylor, Snigdha Panigrahi, J. Marković, 2017, 1703.06154.

Inferring Treatment Effects After Testing Instrument Strength in Linear Models.

Jonathan Taylor, Hyunseung Kang, Nan Bi, 2020, 2003.06723.

The University of Chicago Department of Statistics Seminar Series

R. Tibshirani, Jonathan E. Taylor, 2011 .

Survival analysis on rare events using group-regularized multi-response Cox regression

Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2020, Bioinform..

Exact Post Model Selection Inference for Marginal Screening

Jonathan E. Taylor, Jason D. Lee, Jonathan E. Taylor, 2014, NIPS.

Exact Post-Selection Inference for Sequential Regression Procedures

R. Tibshirani, R. Tibshirani, Jonathan E. Taylor, 2014, 1401.3889.

A General Framework for Estimation and Inference From Clusters of Features

Robert Tibshirani, Jonathan Taylor, Stephen Reid, 2015, 1511.07839.

Statistical learning and selective inference

Jonathan Taylor, Robert J Tibshirani, R. Tibshirani, 2015, Proceedings of the National Academy of Sciences.

Approximate selective inference via maximum likelihood

Jonathan E. Taylor, Snigdha Panigrahi, 2019, Journal of the American Statistical Association.

Integrative methods for post-selection inference under convex constraints

Asaf Weinstein, Snigdha Panigrahi, Jonathan Taylor, 2019, The Annals of Statistics.

A SELECTIVE SURVEY OF SELECTIVE INFERENCE

Jonathan E. Taylor, Jonathan E. Taylor, 2019, Proceedings of the International Congress of Mathematicians (ICM 2018).

Inference after black box selection.

Jonathan E. Taylor, J. M. Taylor, J. Marković, 2019, 1901.09973.

Inferactive data analysis

Jonathan Taylor, Lucy Xia, Jonathan E. Taylor, 2017, Scandinavian Journal of Statistics.

Selective sampling after solving a convex problem

Snigdha Panigrahi, Jonathan Taylor, Jonathan E. Taylor, 2016, 1609.05609.

Bayesian Post-Selection Inference in the Linear Model

Jonathan E. Taylor, Snigdha Panigrahi, A. Weinstein, 2016 .

Asymptotics of Selective Inference

Jonathan Taylor, Jonathan E. Taylor, Xiaoying Tian, 2015, 1501.03588.

Exact Selective Inference with Randomization

Jonathan E. Taylor, Snigdha Panigrahi, Kevin Fry, 2022, 2212.12940.

Black-box Selective Inference via Bootstrapping

Jonathan E. Taylor, J. Marković, Sifan Liu, 2022, 2203.14504.

Post-selection adaptive inference for Least Angle Regression and the Lasso

Robert Tibshirani, Jonathan Taylor, Ryan J. Tibshirani, 2014 .

A tail strength measure for assessing the overall significance in a dataset

R. Tibshirani, Jonathan E. Taylor, 2005 .

The 'miss rate' for the analysis of gene expression data.

Robert Tibshirani, Bradley Efron, Jonathan Taylor, 2005, Biostatistics.

High-dimensional regression adjustments in randomized experiments

Stefan Wager, Robert J Tibshirani, Wenfei Du, 2016, Proceedings of the National Academy of Sciences.

Valid post-correction inference for censored regression problems

Jonathan E. Taylor, Yuekai Sun, 2014, 1403.3457.

Why adaptively collected data have negative bias and how to correct for it pdf

Xinkun Nie, Xiaoying Tian, Jonathan Taylor, 2017, AISTATS.

The solution path of the generalized lasso

R. Tibshirani, Jonathan E. Taylor, 2011 .

Communication-efficient sparse regression: a one-shot approach pdf

Qiang Liu, Jonathan E. Taylor, Jason D. Lee, 2015, ArXiv.

On model selection consistency of regularized M-estimators

Jonathan E. Taylor, J. Lee, Yuekai Sun, 2013, ArXiv.

Lasso Degrees of Freedom when p > n

R. Tibshirani, Jonathan E. Taylor, 2011 .

Exact Selective Inference with Randomization

Jonathan E. Taylor, Snigdha Panigrahi, Kevin Fry, 2022, 2212.12940.

The solution path of the generalized lasso

R. Tibshirani, Jonathan E. Taylor, 2011 .

A statistician plays darts

Jonathan Taylor, Andrew Price, Ryan J. Tibshirani, 2011 .

A Lasso for Hierarchical Interactions

R. Tibshirani, Jonathan E. Taylor, 2013 .

Post‐selection inference for ℓ1 ‐penalized likelihood models

R. Tibshirani, Jonathan E. Taylor, 2018, The Canadian journal of statistics = Revue canadienne de statistique.

Bayesian Post-Selection Inference in the Linear Model

Asaf Weinstein, Snigdha Panigrahi, Jonathan Taylor, 2016, 1605.08824.

On model selection consistency of M-estimators with geometrically decomposable penalties

Jonathan E. Taylor, J. Lee, Yuekai Sun, 2013, NIPS 2013.

Sparse Steinian Covariance Estimation

Brett Naul, Jonathan Taylor, Jonathan E. Taylor, 2017 .

Black-box Selective Inference via Bootstrapping

Jonathan E. Taylor, Sifan Liu, Jelena Markovic-Voronov, 2022, 2203.14504.