Double shrunken selection operator
From MaRDI portal
Publication:5086183
DOI10.1080/03610918.2017.1395040OpenAlexW2581138979MaRDI QIDQ5086183
Bahadır Yüzbaşı, Mohammad Arashi
Publication date: 1 July 2022
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1612.06304
Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)
Related Items
A new data adaptive elastic net predictive model using hybridized smoothed covariance estimators with information complexity ⋮ Choosing the optimal hybrid covariance estimators in adaptive elastic net regression models using information complexity ⋮ A nonlinear mixed–integer programming approach for variable selection in linear regression model ⋮ Adaptive estimation strategies in gamma regression model
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- Estimation of the mean of a multivariate normal distribution
- Siegel's formula via Stein's identities
- Asymptotics for Lasso-type estimators.
- Shrinkage estimation strategy in quasi-likelihood models
- Penalty, shrinkage and pretest strategies. Variable selection and estimation
- Shrinkage ridge regression in partial linear models
- SHRINKAGE, PRETEST AND ABSOLUTE PENALTY ESTIMATORS IN PARTIALLY LINEAR MODELS
- Estimation of the mean vector of a multivariate normal distribution under symmetry
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- A Family of Minimax Estimators of the Mean of a Multivariate Normal Distribution
- Double shrinkage estimators for large sparse covariance matrices
- Confidence sets based on the positive part James–Stein estimator with the asymptotically constant coverage probability
- Shrinkage and penalized estimation in semi-parametric models with multicollinear data
- Some new methods to solve multicollinearity in logistic regression
- Model Selection and Estimation in Regression with Grouped Variables
- Least Squares Model Averaging
- Theory of Preliminary Test and Stein‐Type Estimation With Applications
- The Risk of James–Stein and Lasso Shrinkage