Adaptive estimation of high-dimensional signal-to-noise ratios
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Publication:1750099
DOI10.3150/17-BEJ975zbMath1415.62034arXiv1602.08006MaRDI QIDQ1750099
Elisabeth Gassiat, Nicolas Verzelen
Publication date: 18 May 2018
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1602.08006
Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Order statistics; empirical distribution functions (62G30)
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Uses Software
Cites Work
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Concentration inequalities for non-Lipschitz functions with bounded derivatives of higher order
- Testing composite hypotheses, Hermite polynomials and optimal estimation of a nonsmooth functional
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- Minimax quadratic estimation of a quadratic functional
- Nonquadratic estimators of a quadratic functional
- Heritability estimation in high dimensional sparse linear mixed models
- Adaptive estimation of high-dimensional signal-to-noise ratios
- Adaptive estimation of a quadratic functional by model selection.
- Nonparametric goodness-of-fit testing under Gaussian models
- Non-asymptotic minimax rates of testing in signal detection
- Detection boundary in sparse regression
- Debiasing the Lasso: optimal sample size for Gaussian designs
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- Minimax estimation of linear and quadratic functionals on sparsity classes
- Goodness-of-fit tests for high-dimensional Gaussian linear models
- Confidence sets in sparse regression
- Variance estimation in high-dimensional linear models
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Scaled sparse linear regression
- Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression
- EigenPrism: Inference for High Dimensional Signal-to-Noise Ratios
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
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