Shrinkage and LASSO strategies in high-dimensional heteroscedastic models

DOI10.1080/03610926.2014.921305zbMath1347.62146OpenAlexW2413619832MaRDI QIDQ2816430

Eric Yu Yin Lin, Sévérien Nkurunziza, Marwan A. Al-Momani

Publication date: 22 August 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2014.921305

zbMATH Keywords

least squares estimatorLASSOheteroscedastic modelslinear regression modelsasymptotic distribution riskHHR estimatorshrinkage strategies

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Parametric inference under constraints (62F30)

Related Items

Generalized autoregressive moving average models: an efficient estimation approach, Pretest and shrinkage estimation of the regression parameter vector of the marginal model with multinomial responses

Cites Work

• Unnamed Item
• Unnamed Item
• A Simple Test for Heteroscedasticity and Random Coefficient Variation
• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• The Adaptive Lasso and Its Oracle Properties
• Absolute penalty and shrinkage estimation in partially linear models
• Shrinkage estimation in general linear models
• Positive shrinkage, improved pretest and absolute penalty estimators in partially linear models
• The risk of pretest and shrinkage estimators
• High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis
• SHRINKAGE, PRETEST AND ABSOLUTE PENALTY ESTIMATORS IN PARTIALLY LINEAR MODELS
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regularization and Variable Selection Via the Elastic Net
• Theory of Preliminary Test and Stein‐Type Estimation With Applications