Stein-rule M-estimation in sparse partially linear models

DOI10.1007/s42081-023-00231-0MaRDI QIDQ6578507

S. E. Ahmed, Shuangzhe Liu, Enayetur Raheem

Publication date: 25 July 2024

Published in: Japanese Journal of Statistics and Data Science (Search for Journal in Brave)

zbMATH Keywords

simulationasymptotic bias and riskfull model and submodel M-estimationStein-rule M-estimation

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Absolute penalty and shrinkage estimation in partially linear models
• Shrinkage estimation for the mean of the inverse Gaussian population
• Robust ridge M-estimators with pretest and Stein-rule shrinkage for an intercept term
• Robust estimators in semiparametric partly linear regression models.
• Portfolio selection: shrinking the time-varying inverse conditional covariance matrix
• Shrinkage estimation strategy in quasi-likelihood models
• Preliminary test and Stein-type shrinkage ridge estimators in robust regression
• Improved preliminary test and Stein-rule Liu estimators for the ill-conditioned elliptical linear regression model
• Penalty, shrinkage and pretest strategies. Variable selection and estimation
• Risk comparison of some shrinkage M-estimators in linear models
• SHRINKAGE, PRETEST AND ABSOLUTE PENALTY ESTIMATORS IN PARTIALLY LINEAR MODELS
• Root-N-Consistent Semiparametric Regression

This page was built for publication: Stein-rule M-estimation in sparse partially linear models