A global homogeneity test for high-dimensional linear regression
DOI10.1214/15-EJS999zbMath1310.62068arXiv1308.3568MaRDI QIDQ2263711
Fanny Villers, Camille Charbonnier, Nicolas Verzelen
Publication date: 19 March 2015
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1308.3568
multiple testinghigh-dimensional statisticsGaussian graphical modeladaptive testingminimax hypothesis testingdetection boundarytwo-sample hypothesis testing
Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15) Paired and multiple comparisons; multiple testing (62J15)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Estimator selection in the Gaussian setting
- Graph Selection with GGMselect
- Sparse inverse covariance estimation with the graphical lasso
- Two sample tests for high-dimensional covariance matrices
- Statistical significance in high-dimensional linear models
- Correction: Rejoinder to ``A significance test for the lasso
- Inferring multiple graphical structures
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- Near-ideal model selection by \(\ell _{1}\) minimization
- High-dimensional variable selection
- Tests for Gaussian graphical models
- Lasso-type recovery of sparse representations for high-dimensional data
- Adaptive tests of linear hypotheses by model selection
- Adaptive estimation of a quadratic functional by model selection.
- Higher criticism for detecting sparse heterogeneous mixtures.
- Least angle regression. (With discussion)
- Minimax risks for sparse regressions: ultra-high dimensional phenomenons
- Inferring sparse Gaussian graphical models with latent structure
- On the conditions used to prove oracle results for the Lasso
- Detection boundary in sparse regression
- Network enrichment analysis in complex experiments
- Goodness-of-fit tests for high-dimensional Gaussian linear models
- A two-sample test for high-dimensional data with applications to gene-set testing
- Simultaneous analysis of Lasso and Dantzig selector
- A test for the mean vector with fewer observations than the dimension
- High-dimensional graphs and variable selection with the Lasso
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- p-Values for High-Dimensional Regression
- Scaled sparse linear regression
- Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
- Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
- Computational sample complexity and attribute-efficient learning
- High-dimensional regression with unknown variance
This page was built for publication: A global homogeneity test for high-dimensional linear regression
