Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares
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Publication:6110712
DOI10.1080/01621459.2021.1893177zbMath1515.62068arXiv1810.01323OpenAlexW3133361820MaRDI QIDQ6110712
Publication date: 6 July 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.01323
signal-to-noise ratioquadratic functionallinear regression modelfraction of variance explainedmoderate dimension
Cites Work
- Unnamed Item
- Innovated higher criticism for detecting sparse signals in correlated noise
- On asymptotically optimal confidence regions and tests for high-dimensional models
- High dimensional robust M-estimation: asymptotic variance via approximate message passing
- Flexible results for quadratic forms with applications to variance components estimation
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- On the impact of predictor geometry on the performance on high-dimensional ridge-regularized generalized robust regression estimators
- The sparsity and bias of the LASSO selection in high-dimensional linear regression
- Lasso-type recovery of sparse representations for high-dimensional data
- Spectral analysis of large dimensional random matrices
- Asymptotic behavior of M-estimators of p regression parameters when \(p^ 2/n\) is large. I. Consistency
- A central limit theorem for generalized quadratic forms
- Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
- The jackknife estimate of variance
- Asymptotics for high dimensional regression \(M\)-estimates: fixed design results
- Adaptive estimation of high-dimensional signal-to-noise ratios
- Higher criticism for detecting sparse heterogeneous mixtures.
- Detection boundary in sparse regression
- The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled Chi-square
- Accuracy assessment for high-dimensional linear regression
- High-dimensional generalized linear models and the lasso
- Confidence sets in sparse regression
- Estimation and confidence sets for sparse normal mixtures
- High-dimensional graphs and variable selection with the Lasso
- Testing linear hypotheses in high-dimensional regressions
- Variance estimation in high-dimensional linear models
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- On robust regression with high-dimensional predictors
- Scaled sparse linear regression
- Mathematical Statistics
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression
- All Invariant Moments of the Wishart Distribution
- Semisupervised Inference for Explained Variance in High Dimensional Linear Regression and its Applications
- Tests for High-Dimensional Regression Coefficients With Factorial Designs
- EigenPrism: Inference for High Dimensional Signal-to-Noise Ratios
- On the Departure from Normality of a Certain Class of Martingales
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix
- On the asymptotic distribution of the Moran \(I\) test stastistic with applications
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