Pages that link to "Item:Q2934105"

The following pages link to Confidence Intervals and Hypothesis Testing for High-Dimensional Regression (Q2934105):

Displaying 50 items.

• Multicarving for high-dimensional post-selection inference (Q2044355) (← links)
• The de-biased group Lasso estimation for varying coefficient models (Q2046473) (← links)
• Second-order Stein: SURE for SURE and other applications in high-dimensional inference (Q2054467) (← links)
• The distribution of the Lasso: uniform control over sparse balls and adaptive parameter tuning (Q2054498) (← links)
• Augmented minimax linear estimation (Q2073703) (← links)
• Scale calibration for high-dimensional robust regression (Q2074316) (← links)
• High-dimensional inference for linear model with correlated errors (Q2075037) (← links)
• In defense of the indefensible: a very naïve approach to high-dimensional inference (Q2075709) (← links)
• Some perspectives on inference in high dimensions (Q2075798) (← links)
• A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables (Q2076131) (← links)
• Confidence intervals for parameters in high-dimensional sparse vector autoregression (Q2076143) (← links)
• Spatially relaxed inference on high-dimensional linear models (Q2080369) (← links)
• Network differential connectivity analysis (Q2080732) (← links)
• Asymptotic normality of robust \(M\)-estimators with convex penalty (Q2106774) (← links)
• Design of c-optimal experiments for high-dimensional linear models (Q2108501) (← links)
• High-dimensional linear models with many endogenous variables (Q2116354) (← links)
• Powerful knockoffs via minimizing reconstructability (Q2119228) (← links)
• Covariate-adjusted inference for differential analysis of high-dimensional networks (Q2121714) (← links)
• Mathematical foundations of machine learning. Abstracts from the workshop held March 21--27, 2021 (hybrid meeting) (Q2131208) (← links)
• Testability of high-dimensional linear models with nonsparse structures (Q2131247) (← links)
• Inference for low-rank tensors -- no need to debias (Q2131273) (← links)
• Recent advances in statistical methodologies in evaluating program for high-dimensional data (Q2132738) (← links)
• Sparse matrix linear models for structured high-throughput data (Q2135347) (← links)
• Semi-supervised empirical risk minimization: using unlabeled data to improve prediction (Q2136649) (← links)
• High-dimensional sufficient dimension reduction through principal projections (Q2136660) (← links)
• De-biasing the Lasso with degrees-of-freedom adjustment (Q2136990) (← links)
• Doubly robust semiparametric inference using regularized calibrated estimation with high-dimensional data (Q2137036) (← links)
• The asymptotic distribution of the MLE in high-dimensional logistic models: arbitrary covariance (Q2137045) (← links)
• Post-model-selection inference in linear regression models: an integrated review (Q2137823) (← links)
• Meta-analytic Gaussian network aggregation (Q2141634) (← links)
• Thresholding tests based on affine Lasso to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension (Q2143028) (← links)
• Doubly debiased Lasso: high-dimensional inference under hidden confounding (Q2148976) (← links)
• Hierarchical correction of \(p\)-values via an ultrametric tree running Ornstein-Uhlenbeck process (Q2155000) (← links)
• Single-index composite quantile regression for ultra-high-dimensional data (Q2161022) (← links)
• A unifying framework of high-dimensional sparse estimation with difference-of-convex (DC) regularizations (Q2163076) (← links)
• Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors (Q2172011) (← links)
• Statistical inference for model parameters in stochastic gradient descent (Q2176618) (← links)
• Efficient estimation of linear functionals of principal components (Q2176629) (← links)
• Hierarchical inference for genome-wide association studies: a view on methodology with software (Q2184390) (← links)
• Inference for high-dimensional instrumental variables regression (Q2190211) (← links)
• Debiasing the debiased Lasso with bootstrap (Q2192302) (← links)
• Detangling robustness in high dimensions: composite versus model-averaged estimation (Q2192312) (← links)
• Relaxing the assumptions of knockoffs by conditioning (Q2215771) (← links)
• Ill-posed estimation in high-dimensional models with instrumental variables (Q2227078) (← links)
• Robust regression with compositional covariates (Q2242144) (← links)
• Innovated scalable efficient inference for ultra-large graphical models (Q2244522) (← links)
• A significance test for the lasso (Q2249837) (← links)
• Discussion: ``A significance test for the lasso'' (Q2249838) (← links)
• Rejoinder: ``A significance test for the lasso'' (Q2249839) (← links)
• A global homogeneity test for high-dimensional linear regression (Q2263711) (← links)