One-step regularized estimator for high-dimensional regression models

Cites Work

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On asymptotically optimal confidence regions and tests for high-dimensional models
Nearly unbiased variable selection under minimax concave penalty
Asymptotic normality and optimalities in estimation of large Gaussian graphical models
Exact post-selection inference, with application to the Lasso
A general theory of hypothesis tests and confidence regions for sparse high dimensional models
Statistics for high-dimensional data. Methods, theory and applications.
The sparsity and bias of the LASSO selection in high-dimensional linear regression
Variable selection in nonparametric additive models
Nonparametric maximum likelihood estimation by the method of sieves
Estimating the dimension of a model
Convergence rate of sieve estimates
High-dimensional simultaneous inference with the bootstrap
Asymptotics for Lasso-type estimators.
Debiasing the Lasso: optimal sample size for Gaussian designs
A significance test for the lasso
High-dimensional generalized linear models and the lasso
The Dantzig selector: statistical estimation when $p$ is much larger than $n$. (With discussions and rejoinder).
High-dimensional graphs and variable selection with the Lasso
Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory
Asymptotic properties of the residual bootstrap for Lasso estimators
Bootstrapping Lasso Estimators
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Sieve Extremum Estimates for Weakly Dependent Data
Penalized Composite Quasi-Likelihood for Ultrahigh Dimensional Variable Selection
Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
Double/debiased machine learning for treatment and structural parameters
Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model
Drawing inferences for high‐dimensional linear models: A selection‐assisted partial regression and smoothing approach
Nonconcave Penalized Likelihood With NP-Dimensionality
Model Selection and Estimation in Regression with Grouped Variables
Instrumental Variable Estimation of Nonparametric Models
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
Inference in Additively Separable Models With a High-Dimensional Set of Conditioning Variables

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