Variable selection via the composite likelihood method for multilevel longitudinal data with missing responses and covariates
DOI10.1016/j.csda.2019.01.011OpenAlexW2911672893WikidataQ128481322 ScholiaQ128481322MaRDI QIDQ1737998
Di Shu, Grace Y. Yi, Wenqing He, Haocheng Li
Publication date: 29 March 2019
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2019.01.011
longitudinal datamissing datavariable selectioncomposite likelihoodmissing not at randommultilevel structure
Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Missing data (62D10)
Cites Work
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- The Adaptive Lasso and Its Oracle Properties
- A pairwise likelihood approach for longitudinal data with missing observations in both response and covariates
- Simultaneous variable selection and estimation for multivariate multilevel longitudinal data with both continuous and binary responses
- Variable selection for Cox's proportional hazards model and frailty model
- Joint Variable Selection for Fixed and Random Effects in Linear Mixed-Effects Models
- Multivariate Logistic Models for Incomplete Binary Responses
- Maximum Likelihood Methods for Nonignorable Missing Responses and Covariates in Random Effects Models
- Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
- Variable Selection for Semiparametric Mixed Models in Longitudinal Studies
- Random Effects Probit and Logistic Regression Models for Three-Level Data
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Informative Drop-Out in Longitudinal Data Analysis
- Modeling the Drop-Out Mechanism in Repeated-Measures Studies
- Variable Selection With the Strong Heredity Constraint and Its Oracle Property
- Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data
- Missing Data Mechanisms for Analysing Longitudinal Data with Incomplete Observations in Both Responses and Covariates
- New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis
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