The generalized equivalence of regularization and min-max robustification in linear mixed models
From MaRDI portal
Publication:2062416
DOI10.1007/s00362-020-01214-zzbMath1483.62119OpenAlexW3120962767MaRDI QIDQ2062416
Dennis Kreber, Jan Pablo Burgard, Joscha Krause, Domingo Morales
Publication date: 27 December 2021
Published in: Statistical Papers (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00362-020-01214-z
Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Measurement error in Lasso: impact and likelihood bias correction
- Overview of total least-squares methods
- On the oracle property of adaptive group Lasso in high-dimensional linear models
- Estimation in high-dimensional linear models with deterministic design matrices
- Sparse recovery under matrix uncertainty
- High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
- Characterization of the equivalence of robustification and regularization in linear and matrix regression
- Spatial robust small area estimation
- A coordinate gradient descent method for nonsmooth separable minimization
- Robust estimation of multivariate regression model
- Robust regression: Asymptotics, conjectures and Monte Carlo
- A unified jackknife theory for empirical best prediction with \(M\)-estimation
- A Fay-Herriot model when auxiliary variables are measured with error
- A robust and efficient estimation and variable selection method for partially linear models with large-dimensional covariates
- Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects
- Preliminary test and Stein-type shrinkage ridge estimators in robust regression
- Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator
- Small area estimation under a measurement error bivariate Fay-Herriot model
- Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization
- Theory and Applications of Robust Optimization
- Newton-Raphson and EM Algorithms for Linear Mixed-Effects Models for Repeated-Measures Data
- Measurement error in regression analysis
- Robust Solutions to Least-Squares Problems with Uncertain Data
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Mixed-Effects Models in S and S-PLUS
- Optimization Methods for Large-Scale Machine Learning
- Regularization and Variable Selection Via the Elastic Net
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
This page was built for publication: The generalized equivalence of regularization and min-max robustification in linear mixed models
