Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models
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Publication:5864506
DOI10.1080/07474938.2015.1092798zbMath1491.62065arXiv1308.3201OpenAlexW2221880352MaRDI QIDQ5864506
Publication date: 7 June 2022
Published in: Econometric Reviews (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1308.3201
Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Parametric tolerance and confidence regions (62F25) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Related Items (3)
Honest Confidence Sets for High-Dimensional Regression by Projection and Shrinkage ⋮ On the Length of Post-Model-Selection Confidence Intervals Conditional on Polyhedral Constraints ⋮ Model Selection and Shrinkage: An Overview
Cites Work
- Unnamed Item
- Unnamed Item
- The Adaptive Lasso and Its Oracle Properties
- Forecasting economic time series using targeted predictors
- Valid post-selection inference
- On the distribution of penalized maximum likelihood estimators: the LASSO, SCAD, and thresholding
- On the distribution of the adaptive LASSO estimator
- Asymptotics for Lasso-type estimators.
- Nonconcave penalized likelihood with a diverging number of parameters.
- An alternative to unit root tests: bridge estimators differentiate between nonstationary versus stationary models and select optimal lag
- Confidence sets based on penalized maximum likelihood estimators in Gaussian regression
- Distributional results for thresholding estimators in high-dimensional Gaussian regression models
- Model selection by multiple test procedures
- THE FINITE-SAMPLE DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS AND UNIFORM VERSUS NONUNIFORM APPROXIMATIONS
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- A Statistical View of Some Chemometrics Regression Tools
- MODEL SELECTION AND INFERENCE: FACTS AND FICTION
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