Shrinkage and pretest estimators for longitudinal data analysis under partially linear models

DOI10.1080/10485252.2016.1190358zbMath1407.62136OpenAlexW2468602310MaRDI QIDQ2832015

Grace Y. Yi, Shakhawat Hossain, Baojiang Chen, S. Ejaz Ahmed

Publication date: 4 November 2016

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2016.1190358

zbMATH Keywords

likelihood ratio testlongitudinal datapartially linear modelpretest and shrinkage estimatorsasymptotic bias and risk

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items

Improved estimation in elliptical linear mixed measurement error models ⋮ The risk of tensor Stein-rules in elliptically contoured distributions ⋮ Improved estimation in tensor regression with multiple change-points ⋮ Pretest and shrinkage estimators in generalized partially linear models with application to real data ⋮ Shrinkage estimation in linear mixed models for longitudinal data ⋮ Non-penalty shrinkage estimation of random effect models for longitudinal data with AR(1) errors ⋮ Unnamed Item ⋮ Estimation strategy of multilevel model for ordinal longitudinal data ⋮ Optimal shrinkage estimations in partially linear single-index models for binary longitudinal data

Cites Work

• Longitudinal data analysis using generalized linear models
• Semiparametric marginal and association regression methods for clustered binary data
• Absolute penalty and shrinkage estimation in partially linear models
• Positive shrinkage, improved pretest and absolute penalty estimators in partially linear models
• Shrinkage estimation in nonlinear regression: The Box-Cox transformation
• Penalty, shrinkage and pretest strategies. Variable selection and estimation
• Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data
• Variable selection in semiparametric regression modeling
• Analysis of correlated binary data under partially linear single-index logistic models
• Variable selection using MM algorithms
• Risk comparison of some shrinkage M-estimators in linear models
• Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error
• SHRINKAGE, PRETEST AND ABSOLUTE PENALTY ESTIMATORS IN PARTIALLY LINEAR MODELS
• Semiparametric Regression for Clustered Data Using Generalized Estimating Equations
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters
• RATES OF CONVERGENCE IN SEMI‐PARAMETRIC MODELLING OF LONGITUDINAL DATA
• L₁ penalty and shrinkage estimation in partially linear models with random coefficient autoregressive errors
• Generalised partial linear single-index mixed models for repeated measures data
• An asymptotic theory for model selection inference in general semiparametric problems
• New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis
• Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data
• Partial Linear Regression Models for Clustered Data