Inference without compatibility: using exponential weighting for inference on a parameter of a linear model
From MaRDI portal
Publication:2040072
DOI10.3150/20-BEJ1280zbMath1473.62251OpenAlexW3160524352WikidataQ115488543 ScholiaQ115488543MaRDI QIDQ2040072
Publication date: 9 July 2021
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3150/20-bej1280
Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric estimation (62G05)
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Exact post-selection inference, with application to the Lasso
- Statistics for high-dimensional data. Methods, theory and applications.
- Exponential screening and optimal rates of sparse estimation
- A tail inequality for quadratic forms of subgaussian random vectors
- Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it
- High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi}
- On the conditions used to prove oracle results for the Lasso
- Debiasing the Lasso: optimal sample size for Gaussian designs
- Variance estimation in high-dimensional linear models
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- Scaled sparse linear regression
- A study of error variance estimation in Lasso regression
- Information Theory and Mixing Least-Squares Regressions
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression
- Semisupervised Inference for Explained Variance in High Dimensional Linear Regression and its Applications
- Double/debiased machine learning for treatment and structural parameters
- Fixed Effects Testing in High-Dimensional Linear Mixed Models
- EigenPrism: Inference for High Dimensional Signal-to-Noise Ratios
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
