Thresholding tests based on affine Lasso to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension
From MaRDI portal
Publication:2143028
DOI10.1016/j.csda.2022.107507OpenAlexW4225410102MaRDI QIDQ2143028
Sylvain Sardy, Caroline Giacobino, Jairo Diaz-Rodriguez
Publication date: 30 May 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2022.107507
Uses Software
Cites Work
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Quantile universal threshold
- Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control
- Thresholding least-squares inference in high-dimensional regression models
- Statistics for high-dimensional data. Methods, theory and applications.
- The solution path of the generalized lasso
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- High-dimensional variable selection
- Higher criticism for detecting sparse heterogeneous mixtures.
- Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences
- Detection boundary in sparse regression
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- A significance test for the lasso
- The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled Chi-square
- Bootstrapping Lasso-type estimators in regression models
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- p-Values for High-Dimensional Regression
- Adaptive Posterior Mode Estimation of a Sparse Sequence for Model Selection
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Linear Hypothesis Testing in Dense High-Dimensional Linear Models
- Approximation Theorems of Mathematical Statistics
- Score Tests for Regression Models
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- L 1-Regularization Path Algorithm for Generalized Linear Models
- The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms
- Tests for High Dimensional Generalized Linear Models
- Model Selection and Estimation in Regression with Grouped Variables
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses
- THE TRANSFORMATION OF POISSON, BINOMIAL AND NEGATIVE-BINOMIAL DATA
This page was built for publication: Thresholding tests based on affine Lasso to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension
