The following pages link to A significance test for the lasso (Q2249837):
Displaying 18 items.
- A weak‐signal‐assisted procedure for variable selection and statistical inference with an informative subsample (Q6076512) (← links)
- Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors (Q6099545) (← links)
- Tuning parameter selection for penalized estimation via \(R^2\) (Q6115527) (← links)
- Distributionally robust and generalizable inference (Q6145146) (← links)
- Inference for sparse linear regression based on the leave-one-covariate-out solution path (Q6164734) (← links)
- Post-selection inference via algorithmic stability (Q6183754) (← links)
- Efficient estimation of the maximal association between multiple predictors and a survival outcome (Q6183767) (← links)
- Carving model-free inference (Q6183866) (← links)
- A new paradigm for high-dimensional data: distance-based semiparametric feature aggregation framework via between-subject attributes (Q6536927) (← links)
- Confidently Comparing Estimates with the c-value (Q6567894) (← links)
- Variable selection using P-splines (Q6604438) (← links)
- Feature-specific inference for penalized regression using local false discovery rates (Q6617496) (← links)
- One-step regularized estimator for high-dimensional regression models (Q6621326) (← links)
- High-dimensional longitudinal classification with the multinomial fused Lasso (Q6625640) (← links)
- A surrogate \(\ell_0\) sparse Cox's regression with applications to sparse high-dimensional massive sample size time-to-event data (Q6627484) (← links)
- SuRF: A new method for sparse variable selection, with application in microbiome data analysis (Q6627935) (← links)
- A sequential rejection testing method for high-dimensional regression with correlated variables (Q6632724) (← links)
- Asymptotically faster estimation of high-dimensional additive models using subspace learning (Q6641032) (← links)
