Significance testing in non-sparse high-dimensional linear models
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Publication:1616315
DOI10.1214/18-EJS1443zbMath1416.62305arXiv1610.02122OpenAlexW2751732267WikidataQ129128194 ScholiaQ129128194MaRDI QIDQ1616315
Publication date: 1 November 2018
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1610.02122
Related Items (15)
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Uses Software
Cites Work
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- A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
- On asymptotically optimal confidence regions and tests for high-dimensional models
- A lava attack on the recovery of sums of dense and sparse signals
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- Statistics for high-dimensional data. Methods, theory and applications.
- A Bernstein type inequality and moderate deviations for weakly dependent sequences
- Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness and sensitivity analysis
- Minimax risk over \(l_ p\)-balls for \(l_ q\)-error
- Comments on: ``High-dimensional simultaneous inference with the bootstrap
- Sharp adaptation for inverse problems with random noise
- Unified methods for censored longitudinal data and causality
- Detection boundary in sparse regression
- Debiasing the Lasso: optimal sample size for Gaussian designs
- Optimal adaptive estimation of linear functionals under sparsity
- Confidence intervals for high-dimensional linear regression: minimax rates and adaptivity
- Minimax estimation of linear and quadratic functionals on sparsity classes
- Simultaneous analysis of Lasso and Dantzig selector
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory
- Reconstruction From Anisotropic Random Measurements
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Adapting to Unknown Smoothness via Wavelet Shrinkage
- Scaled sparse linear regression
- Estimating Regression Models with Multiplicative Heteroscedasticity
- Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
- The Asymptotic Variance of Semiparametric Estimators
- Asymptotic Statistics
- Semiparametric Regression for Repeated Outcomes with Nonignorable Nonresponse
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
- Semiparametric Efficiency in Multivariate Regression Models with Missing Data
- Doubly Robust and Efficient Estimators for Heteroscedastic Partially Linear Single-Index Models Allowing high Dimensional Covariates
- Doubly robust learning for estimating individualized treatment with censored data
- Pivotal Estimation in High-Dimensional Regression via Linear Programming
- Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
- EigenPrism: Inference for High Dimensional Signal-to-Noise Ratios
- Testing Statistical Hypotheses
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- Linear programming. Foundations and extensions
- Estimates for the distribution of sums and maxima of sums of random variables without the Cramér condition
- Ridge regression and asymptotic minimax estimation over spheres of growing dimension
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