Projection-based Inference for High-dimensional Linear Models
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Publication:5066781
DOI10.5705/ss.202019.0283OpenAlexW3176634219MaRDI QIDQ5066781
Publication date: 30 March 2022
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.5705/ss.202019.0283
Uses Software
Cites Work
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Exact post-selection inference, with application to the Lasso
- Valid post-selection inference
- Rates of convergence of the adaptive LASSO estimators to the oracle distribution and higher order refinements by the bootstrap
- Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- Statistics for high-dimensional data. Methods, theory and applications.
- High-dimensional variable selection
- Controlling the false discovery rate via knockoffs
- Significance testing in non-sparse high-dimensional linear models
- A unified theory of confidence regions and testing for high-dimensional estimating equations
- High-dimensional simultaneous inference with the bootstrap
- Debiasing the Lasso: optimal sample size for Gaussian designs
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- A significance test for the lasso
- A knockoff filter for high-dimensional selective inference
- High-dimensional graphs and variable selection with the Lasso
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- p-Values for High-Dimensional Regression
- Bootstrapping Lasso Estimators
- Linear Hypothesis Testing in Dense High-Dimensional Linear Models
- A study of error variance estimation in Lasso regression
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Stability Selection
- Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- High-dimensional empirical likelihood inference