A significance test for the lasso

From MaRDI portal
Publication:2249837

DOI10.1214/13-AOS1175zbMath1305.62254arXiv1301.7161OpenAlexW2172185584WikidataQ43093047 ScholiaQ43093047MaRDI QIDQ2249837

Richard A. Lockhart, Ryan J. Tibshirani, Robert Tibshirani, Jonathan E. Taylor

Publication date: 3 July 2014

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1301.7161



Related Items

A weak‐signal‐assisted procedure for variable selection and statistical inference with an informative subsample, Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors, Tuning parameter selection for penalized estimation via \(R^2\), False Discovery Rate Control via Data Splitting, Distributionally robust and generalizable inference, Inference for sparse linear regression based on the leave-one-covariate-out solution path, Post-selection inference via algorithmic stability, Efficient estimation of the maximal association between multiple predictors and a survival outcome, Carving model-free inference, Conformal Prediction Credibility Intervals, On the impact of model selection on predictor identification and parameter inference, Markov Neighborhood Regression for High-Dimensional Inference, An additive Cox model for coronary heart disease study, Identification of biomarker‐by‐treatment interactions in randomized clinical trials with survival outcomes and high‐dimensional spaces, Partitioned Approach for High-dimensional Confidence Intervals with Large Split Sizes, Regularized projection score estimation of treatment effects in high-dimensional quantile regression, Familywise error rate control via knockoffs, Testing Shape Constraints in Lasso Regularized Joinpoint Regression, Inference in adaptive regression via the Kac-Rice formula, A scalable surrogate \(L_0\) sparse regression method for generalized linear models with applications to large scale data, Mathematical foundations of machine learning. Abstracts from the workshop held March 21--27, 2021 (hybrid meeting), Confidence intervals for high-dimensional partially linear single-index models, Exact post-selection inference, with application to the Lasso, SLOPE is adaptive to unknown sparsity and asymptotically minimax, A unified theory of confidence regions and testing for high-dimensional estimating equations, Post-model-selection inference in linear regression models: an integrated review, Regularization techniques in joinpoint regression, High-Dimensional Inference for Cluster-Based Graphical Models, Thresholding tests based on affine Lasso to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension, Demystifying the bias from selective inference: a revisit to Dawid's treatment selection problem, Powerful test based on conditional effects for genome-wide screening, Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors, Projection-based Inference for High-dimensional Linear Models, Uniform asymptotic inference and the bootstrap after model selection, Scalable methods for Bayesian selective inference, Data shared Lasso: a novel tool to discover uplift, A penalized approach to covariate selection through quantile regression coefficient models, Gene set priorization guided by regulatory networks with p-values through kernel mixed model, Monte Carlo Simulation for Lasso-Type Problems by Estimator Augmentation, Lasso for sparse linear regression with exponentially \(\beta\)-mixing errors, Conditional Test for Ultrahigh Dimensional Linear Regression Coefficients, Penalized expectile regression: an alternative to penalized quantile regression, Rejoinder on: ``Hierarchical inference for genome-wide association studies: a view on methodology with software, Controlling the false discovery rate via knockoffs, Predictor ranking and false discovery proportion control in high-dimensional regression, A nonparametric sequential learning procedure for estimating the pure premium, Statistical Inference, Learning and Models in Big Data, Debiasing the debiased Lasso with bootstrap, Nearly optimal Bayesian shrinkage for high-dimensional regression, Unnamed Item, Statistical proof? The problem of irreproducibility, Statistical learning and selective inference, SLOPE-adaptive variable selection via convex optimization, Unnamed Item, Goodness-of-Fit Tests for High Dimensional Linear Models, A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures, Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso, Unnamed Item, Confidence Intervals for Sparse Penalized Regression With Random Designs, Bayesian Feature Selection with Strongly Regularizing Priors Maps to the Ising Model, A Bootstrap Lasso + Partial Ridge Method to Construct Confidence Intervals for Parameters in High-dimensional Sparse Linear Models, Dynamic tilted current correlation for high dimensional variable screening, Penalized likelihood and multiple testing, Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model, High-dimensional inference in misspecified linear models, A Sequential Significance Test for Treatment by Covariate Interactions, Exact post-selection inference for the generalized Lasso path, Unnamed Item, ROCKET: robust confidence intervals via Kendall's tau for transelliptical graphical models, Efficient test-based variable selection for high-dimensional linear models, Debiasing the Lasso: optimal sample size for Gaussian designs, Logistic regression: from art to science, Online rules for control of false discovery rate and false discovery exceedance, Selective inference with a randomized response, Beyond support in two-stage variable selection, Consistent parameter estimation for Lasso and approximate message passing, Solution paths for the generalized Lasso with applications to spatially varying coefficients regression, Partial penalized empirical likelihood ratio test under sparse case, Variable selection in discrete survival models including heterogeneity, Testing Gaussian process with applications to super-resolution, Network classification with applications to brain connectomics, High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}, Bootstrapping and sample splitting for high-dimensional, assumption-lean inference, Penalized regression for interval-censored times of disease progression: Selection of HLA markers in psoriatic arthritis, False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation, Model selection with mixed variables on the Lasso path, Flexible and Interpretable Models for Survival Data, OR Forum—An Algorithmic Approach to Linear Regression, A permutation approach for selecting the penalty parameter in penalized model selection, Sparse estimation of Cox proportional hazards models via approximated information criteria, Selective inference via marginal screening for high dimensional classification, Simple expressions of the LASSO and SLOPE estimators in low-dimension, Unnamed Item, Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?, High-dimensional confounding adjustment using continuous Spike and Slab priors, A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering, Inference for high-dimensional varying-coefficient quantile regression, Some perspectives on inference in high dimensions, Unnamed Item, PLS for Big Data: a unified parallel algorithm for regularised group PLS, Selection-Corrected Statistical Inference for Region Detection With High-Throughput Assays, Spatially relaxed inference on high-dimensional linear models, A knockoff filter for high-dimensional selective inference, Linear hypothesis testing for high dimensional generalized linear models, Panel data quantile regression with grouped fixed effects, Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation, Bayesian inference for high‐dimensional linear regression under mnet priors, Bootstrap inference for penalized GMM estimators with oracle properties, Valid Model-Free Prediction of Future Insurance Claims


Uses Software


Cites Work