Generalized linear models with structured sparsity estimators
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Publication:6054394
DOI10.1016/j.jeconom.2023.105478arXiv2104.14371OpenAlexW3182873467MaRDI QIDQ6054394
Publication date: 28 September 2023
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2104.14371
Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)
Cites Work
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Estimation and testing under sparsity. École d'Été de Probabilités de Saint-Flour XLV -- 2015
- The benefit of group sparsity in group inference with de-biased scaled group Lasso
- Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models
- Robust inference on average treatment effects with possibly more covariates than observations
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- Oracle inequalities and optimal inference under group sparsity
- Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso
- Honest confidence regions for nonparametric regression
- Causal inference by quantile regression kink designs
- Linear hypothesis testing for high dimensional generalized linear models
- Central limit theorems and bootstrap in high dimensions
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- The Group Lasso for Logistic Regression
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- Asymptotic Confidence Regions for High-Dimensional Structured Sparsity
- High-Dimensional Statistics
- UNIFORM INFERENCE IN HIGH-DIMENSIONAL DYNAMIC PANEL DATA MODELS WITH APPROXIMATELY SPARSE FIXED EFFECTS
- Weakly decomposable regularization penalties and structured sparsity
- Model Selection and Estimation in Regression with Grouped Variables
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
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