Non-penalty shrinkage estimation of random effect models for longitudinal data with AR(1) errors
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Publication:4960759
DOI10.1080/00949655.2018.1511713OpenAlexW2886524654MaRDI QIDQ4960759
Publication date: 23 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2018.1511713
Cites Work
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- Random-Effects Models for Longitudinal Data
- Model selection in linear mixed models
- Parameter estimation and inference in the linear mixed model
- Optimal method in multiple regression with structural changes
- Variable selection for generalized linear mixed models by \(L_1\)-penalized estimation
- Extension of the Gauss-Markov theorem to include the estimation of random effects
- Shrinkage, pretest, and penalty estimators in generalized linear models
- Estimation of variance components in linear mixed measurement error models
- Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data
- Variable selection in linear mixed effects models
- A class of Stein-rules in multivariate regression model with structural changes
- Shrinkage and pretest estimators for longitudinal data analysis under partially linear models
- Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization
- Fixed and Random Effects Selection in Mixed Effects Models
- Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines
- Variable Selection for Semiparametric Mixed Models in Longitudinal Studies
- Mixed-Effects Models in S and S-PLUS
- Adaptive LASSO for linear mixed model selection via profile log-likelihood
- Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters
- Application of shrinkage estimation in linear regression models with autoregressive errors
- Theory of Preliminary Test and Stein‐Type Estimation With Applications
- The Risk of James–Stein and Lasso Shrinkage
- Longitudinal Data Analysis
- Linear mixed models for longitudinal data